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app.py

@@ -0,0 +1,574 @@
+import gradio as gr
+import pandas as pd
+import json
+import os
+from utils.logger import create_log_entry, log_experiment_results
+from utils.file_utils import load_csv, preview_dataframe, get_column_names
+from utils.training import train_models
+from utils.preprocessing import preprocess_data
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler, LabelEncoder
+from sklearn.model_selection import GridSearchCV, RandomizedSearchCV, ParameterGrid, train_test_split
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
+import numpy as np
+from utils.training import get_model_instance
+try:
+    from skopt import BayesSearchCV
+    bayes_available = True
+except ImportError:
+    bayes_available = False
+
+
+session = {
+    "raw_df": None,   
+    "df": None,       
+    "features": [],
+    "target": None,
+    "columns": [],
+    "missing_strategy": "drop",      
+    "transformation_text": ""        
+}
+# ---------------------------
+# Dahsboard 
+# ---------------------------
+
+# ---------------------------
+# Step 1: File Upload Handler
+# ---------------------------
+def handle_upload(file):
+    if file is None:
+        return "No file uploaded", None, gr.update(choices=[]), gr.update(choices=[])
+    try:
+        df, err = load_csv(file.name)
+        session["uploaded_filename"] = file.name
+        if err:
+            return f"Error: {err}", None, gr.update(choices=[]), gr.update(choices=[])
+        session["raw_df"] = df.copy()
+        session["df"] = df.copy()  # Initialize processed df as raw df
+        columns = get_column_names(df)
+        session["columns"] = columns
+        return (
+            "File uploaded successfully!",
+            preview_dataframe(df),
+            gr.update(choices=columns, value=[]),
+            gr.update(choices=columns, value=None)
+        )
+    except Exception as e:
+        return f"Error: {e}", None, gr.update(choices=[]), gr.update(choices=[])
+
+# ---------------------------
+# Step 2: Global Missing Value Strategy
+# ---------------------------
+
+
+def save_missing_strategy(missing_strategy):
+    raw_df = session.get("raw_df")
+    target_col = session.get("target", "")
+    if raw_df is None:
+        return "No data available", None
+    processed_df = preprocess_data(raw_df.copy(), target_col=target_col, missing_strategy=missing_strategy, transformation_map={})
+    session["df"] = processed_df
+    session["missing_strategy"] = missing_strategy  # Store in session
+    return f"Missing value strategy '{missing_strategy}' applied", preview_dataframe(processed_df)
+
+
+# ---------------------------
+# Step 3: Save Features and Target Selection (Filter DataFrame)
+# ---------------------------
+def save_feature_target_selection(features, target):
+    if session.get("df") is None:
+        return "No data available", "", None
+    session["features"] = features
+    session["target"] = target
+    selected_cols = features.copy()
+    if target and target not in selected_cols:
+        selected_cols.append(target)
+    filtered_df = session["df"][selected_cols]
+    session["df"] = filtered_df
+    default_trans = ", ".join(["No Transformation"] * len(features)) if features else ""
+    return f"Selected {len(features)} features and target: {target}", default_trans, preview_dataframe(filtered_df)
+
+# ---------------------------
+# Step 4: Save Transformation Options
+# ---------------------------
+def save_transformation_options(transformation_text):
+    if session.get("df") is None or not session.get("features"):
+        return "No data or features available", None
+    trans_list = [t.strip() for t in transformation_text.split(",")] if transformation_text.strip() != "" else []
+    if len(trans_list) < len(session["features"]):
+        trans_list += ["No Transformation"] * (len(session["features"]) - len(trans_list))
+    transformation_mapping = {session["features"][i]: trans_list[i] for i in range(len(session["features"]))}
+    df = session.get("df").copy()
+    def apply_transformations(df, transformation_map):
+        for col, transform in transformation_map.items():
+            if transform == "Label Encode":
+                if df[col].dtype == "object" or str(df[col].dtype).startswith("category"):
+                    df[col] = LabelEncoder().fit_transform(df[col])
+                else:
+                    df[col] = LabelEncoder().fit_transform(df[col].astype(str))
+            elif transform == "Normalize":
+                scaler = StandardScaler()
+                df[[col]] = scaler.fit_transform(df[[col]])
+        return df
+    processed_df = apply_transformations(df, transformation_mapping)
+    session["df"] = processed_df
+    session["transformation_text"] = transformation_text  # Store in session
+    return "Transformation options applied", preview_dataframe(processed_df)
+
+# ---------------------------
+# Model Training Function
+# ---------------------------
+def train_selected_models(experiment_title, selected_models, lr_c, lr_max_iter, dt_max_depth, dt_min_samples_split,
+                          rf_n_estimators, rf_max_depth, svm_c, svm_kernel, nb_var_smoothing,
+                          train_size):
+    df = session.get("df")
+    features = session.get("features")
+    target = session.get("target")
+    missing_strategy = session.get("missing_strategy", "drop")
+    transformation_text = session.get("transformation_text", "")
+    if df is None or not features or target is None or not selected_models:
+        return "Please ensure data is uploaded, features/target selected, and models chosen."
+    trans_list = [t.strip() for t in transformation_text.split(",")] if transformation_text.strip() != "" else []
+    if len(trans_list) < len(features):
+        trans_list += ["No Transformation"] * (len(features) - len(trans_list))
+    transformation_mapping = {features[i]: trans_list[i] for i in range(len(features))}
+    preprocessing_steps = [f"Missing Value: {missing_strategy}"] + [f"{k}: {v}" for k, v in transformation_mapping.items()]
+    test_size = 1 - train_size
+    if not set(features).issubset(df.columns):
+        return "Selected features not found in the processed data."
+    X = df[features]
+    y = df[target]
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size)
+    model_params = {
+        "Logistic Regression": {"C": lr_c, "max_iter": lr_max_iter},
+        "Decision Tree": {"max_depth": dt_max_depth, "min_samples_split": dt_min_samples_split},
+        "Random Forest": {"n_estimators": rf_n_estimators, "max_depth": rf_max_depth},
+        "SVM": {"C": svm_c, "kernel": svm_kernel},
+        "Naive Bayes": {"var_smoothing": nb_var_smoothing}
+    }
+    training_logs = train_models(X_train, X_test, y_train, y_test, selected_models, model_params, preprocessing_steps)
+    session["trained_models"] = {model: training_logs[model]["model"] for model in selected_models}
+    session["X_test"] = X_test
+    session["y_test"] = y_test
+    experiment_logs = []
+    for model_name in selected_models:
+        entry = create_log_entry(
+            experiment_title,
+            model_name,
+            model_params[model_name],
+            "",
+            preprocessing_steps,
+            training_logs[model_name]["metrics"],
+            training_logs[model_name].get("training_time", 0),
+            training_logs[model_name]["model"]
+        )
+        experiment_logs.append(entry)
+    log_experiment_results(experiment_logs)
+    formatted_results = "\n".join([f"{model}: {training_logs[model]['metrics']}" for model in selected_models])
+    return formatted_results
+
+# ---------------------------
+# Hyperparameter Tuning Function (Grid Search Example)
+# ---------------------------
+def run_hyperparameter_tuning(experiment_title, selected_models):
+    df = session.get("df")
+    features = session.get("features")
+    target = session.get("target")
+
+    if df is None or not features or target is None or not selected_models:
+        return "Please ensure data is uploaded, features/target selected, and models chosen.", None
+
+    X = df[features]
+    y = df[target]
+
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+
+    strategy_map = {
+        "Grid Search": GridSearchCV,
+        "Random Search": RandomizedSearchCV
+    }
+    if bayes_available:
+        from skopt import BayesSearchCV
+        strategy_map["Bayesian Optimization"] = BayesSearchCV
+
+    param_grids = {
+        "Logistic Regression": {"C": [0.01, 0.1, 1, 10], "max_iter": [100, 200, 300]},
+        "Decision Tree": {"max_depth": [3, 5, 10, None], "min_samples_split": [2, 5, 10]},
+        "Random Forest": {"n_estimators": [50, 100, 200], "max_depth": [None, 10, 20]},
+        "SVM": {"C": [0.1, 1, 10], "kernel": ["linear", "rbf"]},
+        "Naive Bayes": {"var_smoothing": np.logspace(-10, -8, 5)}
+    }
+
+    all_logs = []
+    status_texts = []
+
+    for model_name in selected_models:
+        best_overall_score = -1
+        best_overall_summary = None
+
+        for strategy_name, strategy_cls in strategy_map.items():
+            try:
+                model = get_model_instance(model_name, {})
+
+                if strategy_name == "Grid Search":
+                    searcher = strategy_cls(
+                        model,
+                        param_grid=param_grids[model_name],
+                        scoring="accuracy",
+                        cv=5
+                    )
+                elif strategy_name == "Random Search":
+                    searcher = strategy_cls(
+                        model,
+                        param_distributions=param_grids[model_name],
+                        scoring="accuracy",
+                        cv=5,
+                        n_iter=min(10, len(list(ParameterGrid(param_grids[model_name]))))
+                    )
+                elif strategy_name == "Bayesian Optimization":
+                    searcher = strategy_cls(
+                        model,
+                        search_spaces=param_grids[model_name],
+                        scoring="accuracy",
+                        cv=5,
+                        n_iter=10
+                    )
+                else:
+                    continue
+
+                searcher.fit(X_train, y_train)
+                best_estimator = searcher.best_estimator_
+                best_params = searcher.best_params_
+
+                from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
+
+                y_train_pred = best_estimator.predict(X_train)
+                y_test_pred = best_estimator.predict(X_test)
+
+                metrics = {
+                    "accuracy_train": accuracy_score(y_train, y_train_pred),
+                    "accuracy_test": accuracy_score(y_test, y_test_pred),
+                    "precision_train": precision_score(y_train, y_train_pred, average='weighted', zero_division=0),
+                    "precision_test": precision_score(y_test, y_test_pred, average='weighted', zero_division=0),
+                    "recall_train": recall_score(y_train, y_train_pred, average='weighted', zero_division=0),
+                    "recall_test": recall_score(y_test, y_test_pred, average='weighted', zero_division=0),
+                    "f1_score_train": f1_score(y_train, y_train_pred, average='weighted', zero_division=0),
+                    "f1_score_test": f1_score(y_test, y_test_pred, average='weighted', zero_division=0)
+                }
+
+                log_entry = create_log_entry(
+                    experiment_title,
+                    f"Hyperparameter Tuned {model_name} ({strategy_name})",
+                    best_params,
+                    "",
+                    [f"Strategy: {strategy_name}"],
+                    metrics,
+                    0,
+                    best_estimator
+                )
+                all_logs.append(log_entry)
+
+                if searcher.best_score_ > best_overall_score:
+                    best_overall_score = searcher.best_score_
+                    best_overall_summary = f"{model_name} ({strategy_name}):\n" + "\n".join(
+                        [f"{k}: {v:.4f}" for k, v in metrics.items()]
+                    )
+
+            except Exception as e:
+                continue
+
+        if best_overall_summary:
+            status_texts.append(best_overall_summary)
+        else:
+            status_texts.append(f"{model_name}: All tuning strategies failed.")
+
+    log_experiment_results(all_logs)
+    return "\n\n".join(status_texts), "Tuning complete!"
+
+
+
+
+###--------------------dahsboard 
+
+
+###--------------------dahsboard 
+
+
+
+
+# ---------------------------
+# Gradio Interface Layout
+# ---------------------------
+with gr.Blocks() as demo:
+    gr.Markdown("## ML Model Builder")
+    
+    with gr.Tab("Data Upload & Preprocessing"):
+        # Step 1: File Upload
+        gr.Markdown("### Step 1: Upload File")
+        with gr.Row():
+            file_input = gr.File(label="Upload CSV File", file_types=[".csv"])
+            upload_status = gr.Textbox(label="Upload Status", interactive=False)
+        df_preview = gr.Dataframe(label="Raw Data Preview", interactive=False)
+        
+        # Step 2: Global Missing Value Strategy
+        gr.Markdown("### Step 2: Global Missing Value Strategy")
+        missing_strategy_dropdown = gr.Dropdown(
+            label="Missing Value Strategy",
+            choices=["drop", "mean", "median", "mode"],
+            value="drop",
+            info="Select how to handle missing values for all columns."
+        )
+        save_missing_btn = gr.Button("Save Missing Value Strategy")
+        missing_status = gr.Textbox(label="Missing Strategy Status", interactive=False)
+        missing_preview = gr.Dataframe(label="Data Preview after Missing Strategy", interactive=False)
+        
+        # Step 3: Select Features and Target
+        gr.Markdown("### Step 3: Select Features and Target")
+        feature_selector = gr.CheckboxGroup(label="Select Input Features", choices=[], interactive=True)
+        target_selector = gr.Dropdown(label="Select Target Column", choices=[], interactive=True)
+        save_features_btn = gr.Button("Save Features and Target")
+        features_status = gr.Textbox(label="Features/Target Status", interactive=False)
+        features_preview = gr.Dataframe(label="Data Preview after Feature Selection", interactive=False)
+        
+        # Step 4: Transformation Options
+        gr.Markdown("### Step 4: Transformation Options")
+        gr.Markdown(
+            "For each selected feature (in order), specify a transformation. Allowed options: **No Transformation**, **Label Encode**, **Normalize**. "
+            "Enter your choices as a comma-separated list. E.g.: No Transformation, Label Encode, Normalize"
+        )
+        transformation_text = gr.Textbox(label="Transformation Options", placeholder="E.g. No Transformation, Label Encode, Normalize", lines=1)
+        save_transformation_btn = gr.Button("Save Transformation Options")
+        transformation_status = gr.Textbox(label="Transformation Status", interactive=False)
+        transformation_preview = gr.Dataframe(label="Data Preview after Transformation", interactive=False)
+    
+    with gr.Tab("Model Training"):
+        gr.Markdown("### Model Training and Experiment Logging")
+        # Global Experiment Title Input
+        experiment_title_input = gr.Textbox(label="Experiment Title", placeholder="Enter a title for this experiment", lines=1)
+        
+        gr.Markdown("### Model Selection and Hyperparameter Tuning")
+        model_selector = gr.CheckboxGroup(
+            label="Select Models to Train",
+            choices=["Logistic Regression", "Decision Tree", "Random Forest", "SVM", "Naive Bayes"],
+            value=[], interactive=True
+        )
+        with gr.Column(visible=False) as lr_col:
+            gr.Markdown("**Logistic Regression**")
+            lr_c = gr.Slider(0.01, 10.0, step=0.01, value=1.0, label="C", interactive=True)
+            lr_max_iter = gr.Slider(50, 500, step=10, value=100, label="Max Iterations", interactive=True)
+        with gr.Column(visible=False) as dt_col:
+            gr.Markdown("**Decision Tree**")
+            dt_max_depth = gr.Slider(1, 50, step=1, value=10, label="Max Depth", interactive=True)
+            dt_min_samples_split = gr.Slider(2, 10, step=1, value=2, label="Min Samples Split", interactive=True)
+        with gr.Column(visible=False) as rf_col:
+            gr.Markdown("**Random Forest**")
+            rf_n_estimators = gr.Slider(10, 200, step=10, value=100, label="N Estimators", interactive=True)
+            rf_max_depth = gr.Slider(1, 50, step=1, value=10, label="Max Depth", interactive=True)
+        with gr.Column(visible=False) as svm_col:
+            gr.Markdown("**SVM**")
+            svm_c = gr.Slider(0.01, 10.0, step=0.01, value=1.0, label="C", interactive=True)
+            svm_kernel = gr.Radio(["linear", "poly", "rbf", "sigmoid"], value="rbf", label="Kernel", interactive=True)
+        with gr.Column(visible=False) as nb_col:
+            gr.Markdown("**Naive Bayes**")
+            nb_var_smoothing = gr.Slider(1e-10, 1e-5, step=1e-10, value=1e-9, label="Var Smoothing", interactive=True)
+    
+        model_columns = {
+            "Logistic Regression": lr_col,
+            "Decision Tree": dt_col,
+            "Random Forest": rf_col,
+            "SVM": svm_col,
+            "Naive Bayes": nb_col,
+        }
+    
+        def toggle_model_ui(selected_models):
+            updates = []
+            for model_name, panel in model_columns.items():
+                updates.append(gr.update(visible=(model_name in selected_models)))
+            return updates
+    
+        model_selector.change(
+            fn=toggle_model_ui,
+            inputs=model_selector,
+            outputs=[lr_col, dt_col, rf_col, svm_col, nb_col]
+        )
+    
+        gr.Markdown("### Training Parameters")
+        train_slider = gr.Slider(minimum=0.5, maximum=0.9, step=0.05, value=0.8, label="Training Set Size (proportion)", interactive=True)
+        train_btn = gr.Button("Train Selected Models")
+        training_output = gr.Textbox(label="Training Output", lines=8, interactive=False)
+    
+
+# ---------------------------
+# Define Component Interactions
+# ---------------------------
+    file_input.change(
+        fn=handle_upload,
+        inputs=file_input,
+        outputs=[upload_status, df_preview, feature_selector, target_selector]
+    )
+    
+    save_missing_btn.click(
+        fn=save_missing_strategy,
+        inputs=missing_strategy_dropdown,
+        outputs=[missing_status, missing_preview]
+    )
+    
+    save_features_btn.click(
+        fn=save_feature_target_selection,
+        inputs=[feature_selector, target_selector],
+        outputs=[features_status, transformation_text, features_preview]
+    )
+    
+    save_transformation_btn.click(
+        fn=save_transformation_options,
+        inputs=transformation_text,
+        outputs=[transformation_status, transformation_preview]
+    )
+    
+    train_btn.click(
+        fn=train_selected_models,
+        inputs=[
+            experiment_title_input,
+            model_selector,
+            lr_c, lr_max_iter,
+            dt_max_depth, dt_min_samples_split,
+            rf_n_estimators, rf_max_depth,
+            svm_c, svm_kernel,
+            nb_var_smoothing,
+            train_slider
+        ],
+        outputs=training_output
+    )
+    with gr.Tab("Hyperparameter Tuning"):
+        gr.Markdown("### Fully Automatic Hyperparameter Tuning")
+        gr.Markdown(
+            "This step will automatically tune the selected models using **three search strategies**:\n"
+            "- **Grid Search**\n"
+            "- **Random Search**\n"
+            "- **Bayesian Optimization** (if available)\n\n"
+            "The best-performing result from each strategy will be logged, and the top strategy will be shown below."
+        )
+        experiment_title_hp = gr.Textbox(label="Experiment Title", placeholder="Enter experiment title")
+        model_selector_hp = gr.CheckboxGroup(
+            label="Select Models for Auto-Tuning",
+            choices=["Logistic Regression", "Decision Tree", "Random Forest", "SVM", "Naive Bayes"],
+            value=[], interactive=True
+        )
+        run_tune_btn = gr.Button("Run Hyperparameter Tuning")
+        tuning_output = gr.Textbox(label="Tuning Output", lines=10, interactive=False)
+
+        run_tune_btn.click(
+            fn=run_hyperparameter_tuning,
+            inputs=[experiment_title_hp, model_selector_hp],
+            outputs=[tuning_output, gr.Textbox(visible=False)]
+        )
+    with gr.Tab("Dashboard"):
+        log_df = gr.State(pd.DataFrame())
+
+        def load_log_dataframe_dynamic():
+            import os, json, pandas as pd
+
+            log_path = "experiments/logs/experiment_log.jsonl"
+            if not os.path.exists(log_path):
+                return pd.DataFrame([{"Message": "No logs found. Train or tune a model."}])
+
+            with open(log_path, "r", encoding="utf-8") as f:
+                lines = f.readlines()
+
+            rows = []
+            for line in lines:
+                try:
+                    row = json.loads(line)
+                    metrics = row.get("metrics", {})
+                    entry = {
+                        "Experiment": row.get("experiment_title", ""),
+                        "Timestamp": row.get("timestamp", ""),
+                        "Model": row.get("model", ""),
+                        "Training Time (s)": round(row.get("training_time_sec", 0), 4),
+                        "Inference Time (ms)": round(metrics.get("inference_time", 0) * 1000, 4),
+                        "Model Size (bytes)": row.get("model_size_bytes", ""),
+                        "CPU (%)": row.get("system_info", {}).get("cpu_utilization", ""),
+                        "Memory (MB)": row.get("system_info", {}).get("memory_used_mb", ""),
+                        "CPU Name": row.get("system_info", {}).get("cpu", ""),
+                        "Hyperparameters": json.dumps(row.get("hyperparameters", {})),
+                    }
+                    for k, v in metrics.items():
+                        if k != "inference_time":
+                            entry[k] = round(v, 4) if isinstance(v, (float, int)) else v
+                    rows.append(entry)
+                except Exception as e:
+                    continue
+
+            return pd.DataFrame(rows)
+
+        refresh_button = gr.Button("🔄 Refresh Dashboard")
+        dashboard_table = gr.Dataframe(
+            value=load_log_dataframe_dynamic(),
+            interactive=True,
+            wrap=False,
+            
+        )
+
+        refresh_button.click(
+            fn=load_log_dataframe_dynamic,
+            outputs=dashboard_table,
+        )
+
+    with gr.Tab("Summary"):
+
+        gr.Markdown("### 🔍 Best Models by Metric")
+        gr.Markdown(
+            "- ✅ Automatically finds the **best model** for each evaluation metric from all logged experiments.\n"
+            "- 🔁 Use the **Refresh** button to update this view after new training or tuning."
+        )
+
+        summary_df = gr.Dataframe(label="Top Models by Metric", interactive=False)
+
+        def refresh_summary():
+            import pandas as pd, os, json
+
+            log_path = "experiments/logs/experiment_log.jsonl"
+            if not os.path.exists(log_path):
+                return pd.DataFrame([{"Message": "No logs found. Train or tune a model first."}])
+
+            df = pd.read_json(log_path, lines=True)
+            metric_keys = [
+                "accuracy_test", "precision_test", "recall_test", "f1_score_test"
+            ]
+
+            best_rows = []
+
+            for metric in metric_keys:
+                best = None
+                best_score = -float("inf")
+
+                for _, row in df.iterrows():
+                    score = row.get("metrics", {}).get(metric)
+                    if isinstance(score, (int, float)) and score > best_score:
+                        best = row
+                        best_score = score
+
+                if best is not None:
+                    best_rows.append({
+                        "Metric": metric,
+                        "Best Score": round(best_score, 4),
+                        "Model": best.get("model"),
+                        "Experiment": best.get("experiment_title"),
+                        "Timestamp": best.get("timestamp"),
+                        "Hyperparameters": json.dumps(best.get("hyperparameters", {})),
+                    })
+
+            summary_df_result = pd.DataFrame(best_rows)
+            if not summary_df_result.empty:
+                return summary_df_result
+            else:
+                return pd.DataFrame([{"Message": "No valid metrics found in logs."}])
+
+        refresh_btn = gr.Button("🔁 Refresh")
+        refresh_btn.click(fn=refresh_summary, outputs=summary_df)
+
+        # Load initial data
+        summary_df.value = refresh_summary()
+
+
+demo.launch(ssr_mode=False)

data/iris_synthetic_data.csv

@@ -0,0 +1,3001 @@
+sepal length,sepal width,petal length,petal width,label
+5.2,3.8,1.5,0.3,Iris-setosa
+5.3,4.1,1.5,0.1,Iris-setosa
+4.8,3.1,1.5,0.2,Iris-setosa
+5.2,3.7,1.5,0.2,Iris-setosa
+4.9,3.0,1.5,0.3,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+5.2,3.7,1.5,0.3,Iris-setosa
+5.4,3.4,1.6,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.2,3.7,1.5,0.3,Iris-setosa
+5.0,3.3,1.5,0.2,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+5.4,3.9,1.5,0.4,Iris-setosa
+4.8,3.1,1.5,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.4,3.1,1.1,0.1,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.2,3.4,1.5,0.2,Iris-setosa
+4.5,3.2,1.3,0.2,Iris-setosa
+4.9,3.0,1.5,0.2,Iris-setosa
+4.9,3.4,1.6,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+4.9,3.0,1.5,0.2,Iris-setosa
+4.9,3.0,1.5,0.3,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+5.2,3.4,1.5,0.2,Iris-setosa
+5.4,3.9,1.6,0.4,Iris-setosa
+5.0,3.3,1.6,0.4,Iris-setosa
+5.3,3.9,1.5,0.3,Iris-setosa
+5.1,3.3,1.5,0.2,Iris-setosa
+5.4,4.2,1.5,0.2,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+4.9,3.4,1.8,0.2,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+5.1,3.5,1.5,0.3,Iris-setosa
+5.0,3.3,1.3,0.2,Iris-setosa
+5.4,3.4,1.5,0.2,Iris-setosa
+5.4,3.4,1.5,0.3,Iris-setosa
+5.7,3.8,1.6,0.3,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.6,3.2,1.5,0.2,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+4.6,3.4,1.5,0.3,Iris-setosa
+5.2,3.4,1.5,0.2,Iris-setosa
+4.6,3.4,1.5,0.3,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+5.3,4.0,1.5,0.1,Iris-setosa
+5.4,3.9,1.6,0.4,Iris-setosa
+5.1,3.8,1.6,0.2,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+4.7,3.4,1.5,0.3,Iris-setosa
+4.9,3.0,1.5,0.2,Iris-setosa
+4.7,3.1,1.5,0.3,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+5.5,3.7,1.5,0.2,Iris-setosa
+5.4,3.4,1.6,0.2,Iris-setosa
+5.6,4.4,1.5,0.4,Iris-setosa
+5.3,3.9,1.3,0.4,Iris-setosa
+5.4,3.9,1.6,0.4,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.2,3.7,1.5,0.2,Iris-setosa
+4.3,3.0,1.1,0.1,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+4.9,3.4,1.6,0.2,Iris-setosa
+4.9,3.4,1.6,0.2,Iris-setosa
+5.3,3.9,1.8,0.4,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa
+5.0,3.3,1.6,0.4,Iris-setosa
+5.3,4.0,1.6,0.2,Iris-setosa
+5.0,3.4,1.6,0.4,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.4,3.9,1.5,0.4,Iris-setosa
+5.7,4.4,1.5,0.4,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.7,4.1,1.3,0.2,Iris-setosa
+5.0,3.6,1.5,0.2,Iris-setosa
+5.2,3.4,1.5,0.2,Iris-setosa
+4.5,3.1,1.5,0.2,Iris-setosa
+5.2,3.4,1.5,0.2,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+4.9,3.0,1.5,0.2,Iris-setosa
+5.5,4.2,1.5,0.2,Iris-setosa
+5.1,3.8,1.6,0.4,Iris-setosa
+5.0,3.4,1.6,0.4,Iris-setosa
+4.4,3.1,1.3,0.2,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+4.4,3.1,1.1,0.1,Iris-setosa
+4.8,3.0,1.5,0.1,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.4,2.9,1.5,0.2,Iris-setosa
+4.6,3.4,1.5,0.3,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5.4,3.5,1.3,0.2,Iris-setosa
+4.9,3.1,1.6,0.1,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+5.2,3.7,1.5,0.2,Iris-setosa
+4.6,2.5,1.3,0.3,Iris-setosa
+4.6,3.2,1.5,0.2,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.2,3.4,1.5,0.2,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+4.7,3.2,1.5,0.2,Iris-setosa
+4.6,3.4,1.5,0.3,Iris-setosa
+4.6,3.2,1.5,0.2,Iris-setosa
+5.1,3.5,1.5,0.3,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.5,3.9,1.5,0.4,Iris-setosa
+5.2,4.0,1.5,0.1,Iris-setosa
+5.3,3.5,1.5,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5.0,3.5,1.6,0.6,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+5.1,3.5,1.5,0.2,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+4.5,3.2,1.3,0.2,Iris-setosa
+5.8,4.0,1.1,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.1,3.5,1.5,0.3,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+5.2,3.7,1.5,0.2,Iris-setosa
+5.4,3.9,1.5,0.4,Iris-setosa
+4.9,3.0,1.5,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.8,3.4,1.6,0.2,Iris-setosa
+5.2,3.4,1.5,0.2,Iris-setosa
+5.4,4.0,1.3,0.1,Iris-setosa
+5.1,3.5,1.5,0.2,Iris-setosa
+5.2,3.7,1.6,0.3,Iris-setosa
+4.4,3.0,1.1,0.1,Iris-setosa
+4.9,3.4,1.5,0.2,Iris-setosa
+4.6,3.2,1.5,0.2,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.2,4.0,1.5,0.1,Iris-setosa
+5.4,3.9,1.6,0.4,Iris-setosa
+5.2,3.8,1.8,0.4,Iris-setosa
+5.0,3.2,1.3,0.2,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+5.1,3.9,1.6,0.2,Iris-setosa
+4.9,3.4,1.6,0.2,Iris-setosa
+4.7,3.2,1.5,0.2,Iris-setosa
+5.4,3.9,1.3,0.4,Iris-setosa
+4.6,3.0,1.3,0.1,Iris-setosa
+5.2,4.1,1.5,0.1,Iris-setosa
+4.6,3.2,1.5,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+5.2,3.4,1.5,0.2,Iris-setosa
+4.6,3.1,1.5,0.2,Iris-setosa
+5.1,3.5,1.5,0.2,Iris-setosa
+4.8,3.1,1.5,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.8,3.0,1.5,0.1,Iris-setosa
+5.2,3.4,1.5,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.1,3.5,1.3,0.2,Iris-setosa
+4.7,3.5,1.3,0.2,Iris-setosa
+5.3,3.4,1.5,0.4,Iris-setosa
+5.0,3.6,1.5,0.5,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.7,4.4,1.5,0.4,Iris-setosa
+4.6,3.2,1.5,0.2,Iris-setosa
+5.1,3.5,1.5,0.3,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+5.0,3.6,1.5,0.2,Iris-setosa
+5.3,3.4,1.6,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.3,3.5,1.3,0.2,Iris-setosa
+5.6,4.3,1.5,0.3,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5.3,3.8,1.5,0.4,Iris-setosa
+4.7,3.2,1.5,0.2,Iris-setosa
+5.8,4.0,1.1,0.2,Iris-setosa
+4.9,3.2,1.3,0.2,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa
+5.2,3.4,1.5,0.2,Iris-setosa
+4.4,3.0,1.1,0.1,Iris-setosa
+5.3,3.5,1.3,0.2,Iris-setosa
+5.0,3.4,1.6,0.4,Iris-setosa
+5.1,3.5,1.5,0.2,Iris-setosa
+5.2,3.8,1.5,0.3,Iris-setosa
+5.1,3.5,1.5,0.3,Iris-setosa
+5.0,3.3,1.3,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.0,3.6,1.5,0.2,Iris-setosa
+5.0,3.6,1.5,0.2,Iris-setosa
+4.7,3.1,1.5,0.3,Iris-setosa
+5.2,3.4,1.5,0.2,Iris-setosa
+5.0,3.2,1.3,0.2,Iris-setosa
+4.6,3.2,1.5,0.2,Iris-setosa
+5.0,3.6,1.5,0.2,Iris-setosa
+4.7,3.1,1.5,0.2,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.0,3.2,1.1,0.2,Iris-setosa
+5.8,4.0,1.3,0.2,Iris-setosa
+4.4,3.1,1.3,0.2,Iris-setosa
+5.2,3.7,1.5,0.2,Iris-setosa
+4.6,3.1,1.5,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.6,3.2,1.5,0.2,Iris-setosa
+5.2,4.0,1.5,0.1,Iris-setosa
+4.9,3.4,1.6,0.2,Iris-setosa
+5.7,3.8,1.6,0.3,Iris-setosa
+5.0,3.5,1.3,0.4,Iris-setosa
+4.6,3.1,1.5,0.2,Iris-setosa
+4.8,3.4,1.6,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+5.2,4.0,1.5,0.1,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+4.9,3.4,1.6,0.3,Iris-setosa
+4.8,3.0,1.5,0.1,Iris-setosa
+5.3,3.9,1.8,0.4,Iris-setosa
+5.4,3.4,1.6,0.2,Iris-setosa
+4.6,3.4,1.5,0.3,Iris-setosa
+5.4,3.9,1.5,0.4,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+4.4,3.0,1.3,0.2,Iris-setosa
+5.0,3.5,1.5,0.5,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.0,3.2,1.3,0.2,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.1,3.8,1.6,0.2,Iris-setosa
+4.8,3.2,1.6,0.2,Iris-setosa
+5.0,3.2,1.3,0.2,Iris-setosa
+4.6,3.4,1.5,0.3,Iris-setosa
+5.7,3.8,1.6,0.3,Iris-setosa
+5.2,3.8,1.8,0.4,Iris-setosa
+4.7,3.2,1.6,0.2,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+5.2,3.7,1.5,0.3,Iris-setosa
+4.6,2.5,1.3,0.3,Iris-setosa
+5.2,3.7,1.5,0.2,Iris-setosa
+5.4,3.4,1.3,0.3,Iris-setosa
+4.9,3.5,1.1,0.3,Iris-setosa
+5.1,3.8,1.6,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+5.2,3.8,1.8,0.4,Iris-setosa
+4.8,3.0,1.5,0.1,Iris-setosa
+4.5,3.2,1.3,0.2,Iris-setosa
+5.5,3.5,1.3,0.2,Iris-setosa
+4.8,3.0,1.5,0.3,Iris-setosa
+4.6,2.5,1.3,0.3,Iris-setosa
+4.7,3.2,1.5,0.2,Iris-setosa
+4.8,3.0,1.5,0.1,Iris-setosa
+5.2,3.4,1.5,0.2,Iris-setosa
+5.4,3.4,1.6,0.2,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+4.6,3.1,1.5,0.2,Iris-setosa
+5.1,3.5,1.5,0.4,Iris-setosa
+4.6,3.5,1.3,0.3,Iris-setosa
+5.2,3.8,1.5,0.3,Iris-setosa
+5.0,3.3,1.3,0.2,Iris-setosa
+4.4,2.9,1.5,0.2,Iris-setosa
+5.1,3.6,1.5,0.4,Iris-setosa
+4.8,2.9,1.5,0.1,Iris-setosa
+5.2,3.4,1.5,0.2,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+4.6,3.1,1.5,0.2,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+4.9,3.1,1.6,0.1,Iris-setosa
+5.0,3.6,1.5,0.2,Iris-setosa
+4.6,3.1,1.5,0.2,Iris-setosa
+4.6,3.1,1.5,0.2,Iris-setosa
+4.9,3.0,1.5,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+5.0,3.4,1.6,0.5,Iris-setosa
+5.1,3.8,1.6,0.4,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+5.2,3.4,1.5,0.2,Iris-setosa
+5.0,3.3,1.3,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.1,3.8,1.6,0.2,Iris-setosa
+5.1,3.8,1.8,0.3,Iris-setosa
+4.8,3.4,1.6,0.2,Iris-setosa
+4.9,3.0,1.5,0.2,Iris-setosa
+5.5,3.9,1.5,0.4,Iris-setosa
+4.8,3.0,1.5,0.1,Iris-setosa
+4.4,3.1,1.1,0.1,Iris-setosa
+4.7,3.2,1.5,0.2,Iris-setosa
+5.2,3.7,1.5,0.3,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.2,3.8,1.6,0.2,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+5.2,4.0,1.5,0.1,Iris-setosa
+5.6,3.8,1.6,0.4,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+4.8,3.0,1.5,0.1,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.1,3.3,1.6,0.4,Iris-setosa
+5.7,4.0,1.3,0.2,Iris-setosa
+5.1,3.5,1.5,0.3,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.5,4.1,1.5,0.2,Iris-setosa
+5.0,3.6,1.5,0.2,Iris-setosa
+5.3,3.5,1.5,0.3,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.4,3.4,1.5,0.3,Iris-setosa
+5.0,3.2,1.3,0.2,Iris-setosa
+5.3,3.7,1.5,0.3,Iris-setosa
+5.0,3.2,1.1,0.2,Iris-setosa
+4.9,3.4,1.6,0.2,Iris-setosa
+5.2,3.8,1.5,0.3,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+5.0,3.4,1.5,0.3,Iris-setosa
+5.1,3.5,1.5,0.2,Iris-setosa
+5.2,3.4,1.5,0.3,Iris-setosa
+4.9,3.4,1.5,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5.4,4.2,1.5,0.2,Iris-setosa
+5.2,3.4,1.5,0.2,Iris-setosa
+5.1,3.8,1.8,0.3,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+4.9,3.0,1.5,0.1,Iris-setosa
+5.4,3.4,1.5,0.3,Iris-setosa
+4.7,3.2,1.5,0.2,Iris-setosa
+5.0,3.5,1.5,0.4,Iris-setosa
+5.1,3.5,1.5,0.2,Iris-setosa
+4.6,3.4,1.5,0.3,Iris-setosa
+5.0,3.5,1.5,0.3,Iris-setosa
+4.9,3.0,1.5,0.2,Iris-setosa
+4.9,3.0,1.5,0.2,Iris-setosa
+4.7,3.4,1.5,0.3,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.1,3.5,1.5,0.3,Iris-setosa
+5.2,3.4,1.5,0.2,Iris-setosa
+5.4,3.4,1.3,0.3,Iris-setosa
+5.0,3.6,1.5,0.2,Iris-setosa
+5.1,3.5,1.5,0.2,Iris-setosa
+4.8,3.4,1.6,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.1,3.5,1.5,0.2,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.4,3.5,1.3,0.2,Iris-setosa
+5.4,3.5,1.5,0.2,Iris-setosa
+4.4,3.1,1.3,0.1,Iris-setosa
+5.2,3.7,1.5,0.3,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5.3,3.7,1.5,0.3,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.4,3.4,1.5,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.7,3.2,1.5,0.2,Iris-setosa
+4.9,3.0,1.5,0.2,Iris-setosa
+5.3,3.9,1.8,0.4,Iris-setosa
+5.2,4.0,1.5,0.1,Iris-setosa
+4.7,3.2,1.5,0.2,Iris-setosa
+5.0,3.5,1.3,0.4,Iris-setosa
+4.4,3.2,1.3,0.2,Iris-setosa
+5.5,3.5,1.3,0.2,Iris-setosa
+4.4,3.1,1.3,0.2,Iris-setosa
+4.4,2.9,1.5,0.2,Iris-setosa
+5.4,3.4,1.6,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.1,3.6,1.5,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+5.3,4.0,1.3,0.3,Iris-setosa
+4.9,3.0,1.5,0.2,Iris-setosa
+4.4,3.0,1.3,0.1,Iris-setosa
+4.9,3.0,1.5,0.1,Iris-setosa
+4.6,3.2,1.3,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.1,3.3,1.6,0.5,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.2,3.8,1.8,0.4,Iris-setosa
+5.0,3.4,1.5,0.3,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+5.2,3.4,1.5,0.3,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.8,3.0,1.5,0.1,Iris-setosa
+5.2,3.4,1.5,0.2,Iris-setosa
+4.7,3.2,1.5,0.2,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+5.7,4.4,1.5,0.4,Iris-setosa
+5.1,3.6,1.5,0.4,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.8,3.1,1.5,0.3,Iris-setosa
+4.8,3.1,1.5,0.2,Iris-setosa
+4.6,3.0,1.5,0.3,Iris-setosa
+4.9,3.1,1.3,0.1,Iris-setosa
+5.1,3.8,1.6,0.4,Iris-setosa
+5.1,3.5,1.5,0.3,Iris-setosa
+5.0,3.5,1.3,0.4,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+4.6,3.4,1.5,0.3,Iris-setosa
+5.0,3.4,1.5,0.4,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5.1,3.3,1.5,0.2,Iris-setosa
+4.9,3.1,1.6,0.2,Iris-setosa
+4.4,3.0,1.3,0.2,Iris-setosa
+5.3,3.5,1.3,0.2,Iris-setosa
+5.3,3.9,1.5,0.3,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5.1,3.5,1.5,0.2,Iris-setosa
+4.9,3.1,1.3,0.1,Iris-setosa
+4.9,3.4,1.5,0.2,Iris-setosa
+5.1,3.9,1.6,0.2,Iris-setosa
+5.3,4.0,1.6,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+5.2,4.1,1.5,0.1,Iris-setosa
+5.0,3.2,1.1,0.2,Iris-setosa
+5.4,4.2,1.5,0.2,Iris-setosa
+5.4,4.2,1.5,0.2,Iris-setosa
+5.6,4.4,1.5,0.4,Iris-setosa
+5.0,3.4,1.6,0.4,Iris-setosa
+5.6,3.8,1.6,0.3,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+5.4,4.2,1.5,0.1,Iris-setosa
+5.1,3.4,1.5,0.3,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.0,3.5,1.6,0.6,Iris-setosa
+4.4,3.2,1.3,0.2,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+5.4,3.9,1.5,0.4,Iris-setosa
+5.0,3.2,1.3,0.2,Iris-setosa
+5.0,3.4,1.6,0.4,Iris-setosa
+4.4,2.6,1.3,0.3,Iris-setosa
+5.3,3.9,1.5,0.3,Iris-setosa
+5.4,3.4,1.6,0.2,Iris-setosa
+5.0,3.2,1.1,0.2,Iris-setosa
+5.7,3.8,1.6,0.3,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+4.4,3.2,1.3,0.2,Iris-setosa
+4.9,3.6,1.3,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+5.1,3.6,1.5,0.2,Iris-setosa
+5.2,3.7,1.5,0.3,Iris-setosa
+4.7,3.5,1.3,0.2,Iris-setosa
+5.4,3.9,1.6,0.4,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.1,3.8,1.6,0.4,Iris-setosa
+5.4,3.9,1.3,0.4,Iris-setosa
+5.1,3.5,1.5,0.4,Iris-setosa
+5.0,3.4,1.6,0.5,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.8,3.0,1.5,0.3,Iris-setosa
+4.4,3.1,1.1,0.1,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.2,3.4,1.5,0.2,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+5.5,3.5,1.3,0.2,Iris-setosa
+5.7,4.0,1.3,0.2,Iris-setosa
+5.2,3.4,1.5,0.3,Iris-setosa
+4.6,3.5,1.3,0.3,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5.1,3.5,1.5,0.3,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.6,4.0,1.5,0.3,Iris-setosa
+4.9,3.4,1.6,0.3,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+4.9,3.4,1.6,0.2,Iris-setosa
+5.1,3.5,1.5,0.2,Iris-setosa
+5.1,3.3,1.5,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+5.1,3.5,1.5,0.3,Iris-setosa
+5.1,3.5,1.5,0.4,Iris-setosa
+5.4,4.0,1.6,0.4,Iris-setosa
+5.1,3.8,1.6,0.4,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+5.2,3.4,1.5,0.2,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa
+5.2,4.0,1.5,0.1,Iris-setosa
+4.9,3.0,1.5,0.2,Iris-setosa
+5.3,3.7,1.6,0.2,Iris-setosa
+5.7,4.4,1.5,0.4,Iris-setosa
+4.4,3.2,1.3,0.2,Iris-setosa
+5.1,3.5,1.5,0.3,Iris-setosa
+4.9,3.1,1.6,0.1,Iris-setosa
+4.9,3.4,1.6,0.4,Iris-setosa
+5.8,3.9,1.5,0.3,Iris-setosa
+5.2,3.8,1.8,0.4,Iris-setosa
+5.2,3.4,1.5,0.2,Iris-setosa
+5.1,3.3,1.6,0.5,Iris-setosa
+4.6,3.4,1.5,0.3,Iris-setosa
+4.8,3.0,1.5,0.1,Iris-setosa
+4.9,3.0,1.5,0.2,Iris-setosa
+5.3,3.8,1.5,0.4,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+4.4,2.9,1.5,0.2,Iris-setosa
+4.4,3.0,1.3,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.7,3.8,1.6,0.3,Iris-setosa
+5.7,4.1,1.3,0.2,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.4,3.4,1.5,0.4,Iris-setosa
+5.1,3.7,1.5,0.3,Iris-setosa
+5.1,3.5,1.5,0.3,Iris-setosa
+5.8,4.0,1.3,0.2,Iris-setosa
+5.2,3.8,1.5,0.3,Iris-setosa
+4.4,3.0,1.3,0.2,Iris-setosa
+5.3,4.2,1.5,0.1,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa
+5.0,3.2,1.1,0.2,Iris-setosa
+5.5,4.3,1.5,0.3,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.4,3.9,1.5,0.4,Iris-setosa
+5.1,3.8,1.6,0.4,Iris-setosa
+5.0,3.2,1.3,0.2,Iris-setosa
+4.8,3.0,1.5,0.1,Iris-setosa
+4.8,3.0,1.5,0.1,Iris-setosa
+5.6,4.4,1.5,0.4,Iris-setosa
+4.8,3.0,1.3,0.1,Iris-setosa
+5.1,3.5,1.5,0.2,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+5.4,3.4,1.5,0.4,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+4.5,2.9,1.5,0.2,Iris-setosa
+4.4,3.2,1.3,0.2,Iris-setosa
+5.4,3.4,1.6,0.2,Iris-setosa
+4.6,3.4,1.3,0.3,Iris-setosa
+5.5,3.7,1.6,0.3,Iris-setosa
+5.0,3.4,1.6,0.4,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5.5,3.7,1.6,0.2,Iris-setosa
+5.3,4.0,1.3,0.3,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.2,3.7,1.8,0.3,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+5.7,3.8,1.6,0.3,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+4.6,3.2,1.3,0.2,Iris-setosa
+4.9,3.6,1.3,0.2,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.6,4.0,1.5,0.3,Iris-setosa
+5.1,3.5,1.5,0.2,Iris-setosa
+5.5,3.9,1.5,0.4,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.9,3.4,1.6,0.3,Iris-setosa
+5.0,3.6,1.5,0.2,Iris-setosa
+5.2,3.8,1.5,0.3,Iris-setosa
+4.6,3.3,1.5,0.3,Iris-setosa
+5.1,3.5,1.5,0.3,Iris-setosa
+4.4,3.1,1.3,0.2,Iris-setosa
+4.7,3.2,1.5,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.1,3.5,1.5,0.2,Iris-setosa
+5.1,3.8,1.6,0.2,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.3,3.4,1.6,0.2,Iris-setosa
+4.7,3.4,1.5,0.3,Iris-setosa
+5.0,3.5,1.5,0.3,Iris-setosa
+5.0,3.0,1.6,0.2,Iris-setosa
+4.8,3.0,1.5,0.1,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+5.2,3.4,1.5,0.2,Iris-setosa
+5.0,3.4,1.6,0.4,Iris-setosa
+5.1,3.4,1.5,0.3,Iris-setosa
+5.1,3.6,1.5,0.2,Iris-setosa
+5.1,3.5,1.5,0.3,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.1,3.5,1.5,0.2,Iris-setosa
+4.7,3.5,1.3,0.2,Iris-setosa
+4.8,3.1,1.5,0.1,Iris-setosa
+5.0,3.6,1.5,0.2,Iris-setosa
+4.8,3.6,1.1,0.2,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.3,3.4,1.5,0.3,Iris-setosa
+4.6,3.3,1.5,0.3,Iris-setosa
+4.8,3.1,1.5,0.2,Iris-setosa
+5.1,3.5,1.5,0.4,Iris-setosa
+5.0,3.4,1.5,0.3,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.3,3.8,1.5,0.4,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.4,3.4,1.3,0.3,Iris-setosa
+5.0,3.6,1.5,0.2,Iris-setosa
+5.5,4.2,1.5,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.8,3.0,1.5,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.8,3.4,1.6,0.2,Iris-setosa
+5.2,3.8,1.6,0.2,Iris-setosa
+5.3,4.1,1.5,0.1,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.3,3.5,1.3,0.2,Iris-setosa
+4.5,3.2,1.3,0.2,Iris-setosa
+5.5,3.9,1.6,0.4,Iris-setosa
+5.3,3.9,1.8,0.4,Iris-setosa
+5.1,3.4,1.5,0.4,Iris-setosa
+4.9,3.0,1.5,0.1,Iris-setosa
+4.9,3.1,1.6,0.2,Iris-setosa
+5.1,3.5,1.5,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.5,3.1,1.5,0.2,Iris-setosa
+5.3,3.4,1.5,0.3,Iris-setosa
+4.8,3.1,1.5,0.2,Iris-setosa
+5.2,3.8,1.6,0.2,Iris-setosa
+4.9,3.0,1.5,0.1,Iris-setosa
+5.5,3.5,1.3,0.2,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa
+5.0,3.0,1.6,0.2,Iris-setosa
+5.0,3.4,1.6,0.4,Iris-setosa
+5.2,3.4,1.5,0.2,Iris-setosa
+4.6,3.2,1.5,0.2,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.1,3.5,1.5,0.4,Iris-setosa
+4.5,3.1,1.5,0.2,Iris-setosa
+5.0,3.4,1.6,0.5,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.1,3.8,1.6,0.2,Iris-setosa
+4.7,2.8,1.3,0.3,Iris-setosa
+5.4,4.2,1.5,0.1,Iris-setosa
+4.6,3.4,1.5,0.3,Iris-setosa
+4.4,3.2,1.3,0.2,Iris-setosa
+5.0,3.4,1.5,0.4,Iris-setosa
+5.8,4.0,1.3,0.2,Iris-setosa
+5.3,3.4,1.6,0.2,Iris-setosa
+4.6,3.3,1.5,0.3,Iris-setosa
+5.0,3.4,1.6,0.4,Iris-setosa
+5.3,3.7,1.6,0.3,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.4,3.2,1.3,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.7,3.2,1.5,0.2,Iris-setosa
+5.0,3.6,1.5,0.2,Iris-setosa
+4.6,3.4,1.5,0.3,Iris-setosa
+4.6,3.6,0.9,0.2,Iris-setosa
+4.7,3.0,1.5,0.1,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.4,4.2,1.5,0.1,Iris-setosa
+5.1,3.5,1.5,0.3,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+4.8,3.0,1.5,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.8,3.0,1.5,0.3,Iris-setosa
+4.8,3.1,1.5,0.2,Iris-setosa
+4.7,3.0,1.5,0.1,Iris-setosa
+5.1,3.6,1.5,0.2,Iris-setosa
+5.2,3.8,1.5,0.3,Iris-setosa
+4.8,3.0,1.5,0.1,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+4.9,3.0,1.5,0.1,Iris-setosa
+5.2,3.7,1.5,0.3,Iris-setosa
+5.7,4.4,1.5,0.4,Iris-setosa
+5.2,4.0,1.5,0.1,Iris-setosa
+5.0,3.6,1.5,0.5,Iris-setosa
+5.7,4.1,1.3,0.2,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+4.6,3.5,1.3,0.3,Iris-setosa
+5.1,3.5,1.5,0.2,Iris-setosa
+4.9,3.1,1.3,0.1,Iris-setosa
+4.4,3.1,1.3,0.2,Iris-setosa
+5.0,3.3,1.3,0.2,Iris-setosa
+5.2,3.4,1.5,0.2,Iris-setosa
+4.5,3.2,1.3,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.1,3.5,1.5,0.3,Iris-setosa
+4.6,3.2,1.5,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+4.9,3.0,1.5,0.1,Iris-setosa
+4.9,3.0,1.5,0.2,Iris-setosa
+5.0,3.4,1.5,0.4,Iris-setosa
+5.3,3.5,1.3,0.2,Iris-setosa
+4.6,2.5,1.3,0.3,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+4.4,2.9,1.5,0.2,Iris-setosa
+5.0,3.2,1.3,0.2,Iris-setosa
+5.0,3.4,1.5,0.4,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+4.6,3.5,1.1,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.4,3.2,1.3,0.2,Iris-setosa
+4.8,3.0,1.5,0.1,Iris-setosa
+5.1,3.5,1.5,0.4,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+4.8,3.4,1.6,0.2,Iris-setosa
+5.1,3.5,1.5,0.3,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+4.9,3.4,1.6,0.2,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+5.2,3.7,1.6,0.3,Iris-setosa
+5.4,3.4,1.5,0.4,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+5.5,3.9,1.5,0.4,Iris-setosa
+4.9,3.4,1.5,0.2,Iris-setosa
+5.4,3.9,1.5,0.4,Iris-setosa
+4.6,3.4,1.5,0.3,Iris-setosa
+5.3,3.5,1.5,0.2,Iris-setosa
+4.9,3.0,1.5,0.2,Iris-setosa
+4.9,3.0,1.5,0.2,Iris-setosa
+5.2,3.4,1.5,0.2,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5.1,3.8,1.8,0.4,Iris-setosa
+5.3,4.2,1.5,0.1,Iris-setosa
+4.6,3.3,1.5,0.3,Iris-setosa
+4.8,3.6,1.1,0.2,Iris-setosa
+4.9,3.0,1.5,0.1,Iris-setosa
+4.9,3.0,1.5,0.2,Iris-setosa
+5.1,3.5,1.5,0.2,Iris-setosa
+4.6,3.2,1.3,0.2,Iris-setosa
+4.8,3.6,1.1,0.2,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+5.2,3.4,1.5,0.3,Iris-setosa
+5.0,3.3,1.5,0.2,Iris-setosa
+5.3,3.9,1.5,0.3,Iris-setosa
+4.4,2.9,1.5,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.3,3.4,1.6,0.2,Iris-setosa
+4.4,3.0,1.3,0.1,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.4,3.9,1.6,0.4,Iris-setosa
+5.0,3.6,1.5,0.5,Iris-setosa
+4.9,3.0,1.5,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.6,3.4,1.5,0.3,Iris-setosa
+5.1,3.8,1.6,0.2,Iris-setosa
+4.9,3.1,1.6,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.5,3.2,1.3,0.2,Iris-setosa
+5.1,3.5,1.5,0.3,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5.0,3.3,1.3,0.2,Iris-setosa
+5.5,3.5,1.3,0.2,Iris-setosa
+5.4,3.9,1.3,0.4,Iris-setosa
+5.1,3.8,1.6,0.3,Iris-setosa
+4.4,3.2,1.3,0.2,Iris-setosa
+5.8,4.0,1.3,0.2,Iris-setosa
+4.9,3.0,1.5,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.1,3.7,1.6,0.4,Iris-setosa
+5.4,3.4,1.6,0.2,Iris-setosa
+5.7,3.8,1.6,0.3,Iris-setosa
+4.7,3.2,1.5,0.2,Iris-setosa
+4.4,3.1,1.3,0.2,Iris-setosa
+4.4,3.0,1.3,0.1,Iris-setosa
+5.4,4.0,1.5,0.1,Iris-setosa
+5.0,3.3,1.5,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+4.6,3.1,1.5,0.2,Iris-setosa
+5.7,3.8,1.6,0.3,Iris-setosa
+5.3,3.4,1.5,0.2,Iris-setosa
+4.4,3.2,1.3,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+5.1,3.5,1.5,0.4,Iris-setosa
+5.4,3.4,1.6,0.2,Iris-setosa
+5.1,3.4,1.5,0.3,Iris-setosa
+5.5,3.5,1.3,0.2,Iris-setosa
+5.4,3.9,1.6,0.4,Iris-setosa
+5.3,4.0,1.6,0.2,Iris-setosa
+5.2,4.0,1.5,0.1,Iris-setosa
+4.4,3.1,1.1,0.1,Iris-setosa
+5.4,4.0,1.6,0.4,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa
+5.0,3.3,1.3,0.2,Iris-setosa
+4.6,3.4,1.5,0.3,Iris-setosa
+4.4,3.0,1.3,0.2,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa
+5.3,3.7,1.5,0.3,Iris-setosa
+5.5,3.9,1.6,0.4,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+4.8,3.4,1.8,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+4.9,3.4,1.6,0.2,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+5.3,3.7,1.5,0.3,Iris-setosa
+5.4,3.9,1.5,0.4,Iris-setosa
+4.4,3.0,1.3,0.2,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+4.5,2.4,1.3,0.3,Iris-setosa
+4.5,2.3,1.3,0.3,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+4.9,3.0,1.5,0.2,Iris-setosa
+5.3,3.5,1.3,0.2,Iris-setosa
+4.8,3.0,1.5,0.3,Iris-setosa
+5.5,4.2,1.5,0.2,Iris-setosa
+4.4,2.6,1.3,0.2,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+4.6,3.1,1.5,0.2,Iris-setosa
+5.1,3.5,1.5,0.3,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.9,3.5,1.3,0.2,Iris-setosa
+4.4,2.9,1.5,0.2,Iris-setosa
+5.1,3.5,1.5,0.3,Iris-setosa
+5.4,4.2,1.5,0.1,Iris-setosa
+4.9,3.0,1.5,0.3,Iris-setosa
+5.0,3.5,1.5,0.5,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+5.0,3.5,1.6,0.6,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+4.9,3.4,1.8,0.2,Iris-setosa
+5.1,3.3,1.6,0.4,Iris-setosa
+4.6,3.5,1.1,0.2,Iris-setosa
+5.0,3.6,1.5,0.2,Iris-setosa
+5.3,3.8,1.5,0.4,Iris-setosa
+5.1,3.5,1.5,0.2,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5.5,4.2,1.5,0.2,Iris-setosa
+5.3,3.9,1.6,0.4,Iris-setosa
+4.9,3.4,1.8,0.3,Iris-setosa
+5.2,3.4,1.5,0.2,Iris-setosa
+4.6,2.5,1.3,0.3,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.9,3.0,1.5,0.2,Iris-setosa
+4.8,3.4,1.6,0.2,Iris-setosa
+5.7,3.8,1.6,0.3,Iris-setosa
+4.4,3.2,1.3,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5.1,3.5,1.5,0.3,Iris-setosa
+5.4,3.4,1.5,0.3,Iris-setosa
+4.9,3.5,1.3,0.3,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa
+4.7,3.4,1.5,0.3,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.7,3.5,1.3,0.2,Iris-setosa
+5.1,3.5,1.5,0.4,Iris-setosa
+5.3,4.0,1.5,0.1,Iris-setosa
+5.7,3.8,1.6,0.3,Iris-setosa
+5.3,3.9,1.3,0.4,Iris-setosa
+5.4,4.0,1.6,0.4,Iris-setosa
+5.1,3.5,1.5,0.3,Iris-setosa
+4.9,3.2,1.3,0.2,Iris-setosa
+4.4,3.2,1.3,0.2,Iris-setosa
+4.4,3.0,1.3,0.2,Iris-setosa
+4.6,3.5,1.3,0.3,Iris-setosa
+5.0,3.6,1.5,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.1,3.3,1.6,0.4,Iris-setosa
+5.4,3.4,1.5,0.4,Iris-setosa
+5.0,3.4,1.6,0.5,Iris-setosa
+4.8,3.4,1.6,0.2,Iris-setosa
+5.0,3.6,1.5,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+4.6,2.5,1.3,0.3,Iris-setosa
+4.9,3.6,1.3,0.2,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+4.4,3.1,1.3,0.2,Iris-setosa
+4.9,3.0,1.5,0.2,Iris-setosa
+5.1,3.5,1.5,0.2,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+4.7,3.2,1.6,0.2,Iris-setosa
+5.0,3.5,1.5,0.5,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+4.7,3.4,1.5,0.3,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa
+5.1,3.8,1.6,0.3,Iris-setosa
+4.7,3.6,1.1,0.2,Iris-setosa
+5.3,3.9,1.6,0.3,Iris-setosa
+4.9,3.0,1.5,0.2,Iris-setosa
+5.0,3.5,1.6,0.6,Iris-setosa
+4.8,3.1,1.5,0.2,Iris-setosa
+5.4,3.9,1.6,0.4,Iris-setosa
+5.0,3.6,1.5,0.2,Iris-setosa
+4.8,3.0,1.5,0.1,Iris-setosa
+5.5,4.3,1.5,0.3,Iris-setosa
+4.7,3.2,1.5,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.9,3.6,1.3,0.2,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+4.5,2.9,1.5,0.1,Iris-setosa
+4.9,3.1,1.6,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.8,3.0,1.5,0.2,Iris-setosa
+5.6,3.8,1.6,0.4,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5.0,3.6,1.5,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+5.1,3.6,1.5,0.2,Iris-setosa
+5.3,3.4,1.5,0.3,Iris-setosa
+5.3,3.4,1.6,0.2,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+5.1,3.8,1.6,0.2,Iris-setosa
+5.4,3.4,1.6,0.2,Iris-setosa
+5.2,3.7,1.8,0.3,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.1,3.5,1.5,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.4,3.1,1.1,0.1,Iris-setosa
+5.1,3.8,1.6,0.4,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+5.0,3.0,1.6,0.2,Iris-setosa
+4.8,3.0,1.3,0.1,Iris-setosa
+4.8,3.4,1.6,0.2,Iris-setosa
+5.2,4.1,1.5,0.1,Iris-setosa
+4.8,3.4,1.6,0.2,Iris-setosa
+5.3,3.4,1.3,0.2,Iris-setosa
+5.0,3.3,1.3,0.2,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+5.1,3.5,1.5,0.3,Iris-setosa
+4.8,3.0,1.5,0.1,Iris-setosa
+5.1,3.5,1.5,0.3,Iris-setosa
+5.4,3.4,1.5,0.4,Iris-setosa
+4.4,3.1,1.1,0.1,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+5.2,3.7,1.5,0.3,Iris-setosa
+5.0,3.5,1.5,0.5,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.9,3.4,1.5,0.2,Iris-setosa
+4.8,3.4,1.6,0.2,Iris-setosa
+5.5,3.7,1.6,0.3,Iris-setosa
+5.2,3.7,1.5,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+5.1,3.7,1.6,0.4,Iris-setosa
+5.1,3.5,1.5,0.2,Iris-setosa
+4.4,3.0,1.3,0.1,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.1,3.5,1.5,0.2,Iris-setosa
+5.7,4.4,1.5,0.4,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.5,2.9,1.5,0.2,Iris-setosa
+5.1,3.8,1.8,0.4,Iris-setosa
+4.9,3.4,1.6,0.2,Iris-setosa
+4.9,3.0,1.5,0.2,Iris-setosa
+5.0,3.3,1.5,0.2,Iris-setosa
+4.4,3.0,1.1,0.1,Iris-setosa
+5.1,3.5,1.5,0.2,Iris-setosa
+5.0,3.2,1.3,0.2,Iris-setosa
+5.1,3.5,1.3,0.2,Iris-setosa
+5.1,3.5,1.5,0.2,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa
+4.7,3.5,1.3,0.2,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+5.5,4.2,1.5,0.2,Iris-setosa
+4.8,3.4,1.8,0.2,Iris-setosa
+5.6,4.4,1.5,0.4,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.9,3.4,1.5,0.2,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+5.1,3.7,1.5,0.3,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.3,3.9,1.6,0.3,Iris-setosa
+5.4,3.4,1.6,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.9,3.0,1.5,0.3,Iris-setosa
+5.4,2.4,3.6,1.1,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+5.7,3.0,4.5,1.4,Iris-versicolor
+5.3,2.5,3.4,1.2,Iris-versicolor
+5.7,3.0,3.9,1.3,Iris-versicolor
+5.4,2.3,3.9,1.3,Iris-versicolor
+5.3,2.4,3.6,1.1,Iris-versicolor
+5.9,3.2,4.8,1.8,Iris-versicolor
+6.3,2.3,4.5,1.3,Iris-versicolor
+5.3,2.2,3.8,1.2,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+4.9,2.4,3.2,1.0,Iris-versicolor
+5.5,3.0,4.5,1.5,Iris-versicolor
+5.6,3.0,4.5,1.4,Iris-versicolor
+5.3,2.7,3.9,1.4,Iris-versicolor
+6.2,2.9,4.6,1.4,Iris-versicolor
+5.0,2.1,3.4,1.0,Iris-versicolor
+6.1,3.0,4.6,1.4,Iris-versicolor
+6.2,3.0,4.5,1.4,Iris-versicolor
+5.8,3.0,4.3,1.5,Iris-versicolor
+6.5,2.9,4.5,1.3,Iris-versicolor
+5.5,2.4,3.9,1.1,Iris-versicolor
+5.1,2.5,3.1,1.1,Iris-versicolor
+6.7,3.1,4.5,1.4,Iris-versicolor
+5.7,3.0,4.1,1.2,Iris-versicolor
+6.8,3.1,4.6,1.4,Iris-versicolor
+4.9,2.4,3.2,1.0,Iris-versicolor
+5.7,2.5,3.9,1.3,Iris-versicolor
+5.4,3.0,4.5,1.5,Iris-versicolor
+6.2,2.8,4.5,1.3,Iris-versicolor
+5.7,3.0,4.5,1.4,Iris-versicolor
+5.8,2.8,4.1,1.3,Iris-versicolor
+5.8,2.6,3.9,1.2,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+6.1,2.8,3.9,1.3,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+6.4,2.9,4.3,1.4,Iris-versicolor
+6.2,2.9,4.3,1.3,Iris-versicolor
+6.9,3.1,5.0,1.5,Iris-versicolor
+6.3,3.3,4.6,1.6,Iris-versicolor
+5.9,3.1,4.5,1.7,Iris-versicolor
+5.5,2.4,3.9,1.1,Iris-versicolor
+6.2,2.2,4.5,1.5,Iris-versicolor
+5.6,2.6,4.3,1.2,Iris-versicolor
+5.8,2.3,4.1,1.3,Iris-versicolor
+5.6,2.5,3.8,1.0,Iris-versicolor
+6.7,3.0,5.0,1.7,Iris-versicolor
+6.7,3.1,4.6,1.5,Iris-versicolor
+5.6,3.0,4.5,1.5,Iris-versicolor
+6.1,2.9,4.5,1.4,Iris-versicolor
+5.6,3.0,4.5,1.5,Iris-versicolor
+5.7,2.8,4.3,1.3,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+5.7,2.8,4.5,1.3,Iris-versicolor
+5.5,2.4,3.8,1.1,Iris-versicolor
+6.1,2.9,4.6,1.4,Iris-versicolor
+6.3,3.3,4.6,1.6,Iris-versicolor
+5.8,3.0,4.3,1.5,Iris-versicolor
+5.8,3.0,4.3,1.4,Iris-versicolor
+6.8,3.1,4.8,1.5,Iris-versicolor
+6.3,2.4,4.8,1.5,Iris-versicolor
+5.9,3.2,4.8,1.8,Iris-versicolor
+6.7,3.1,4.5,1.4,Iris-versicolor
+5.6,3.0,4.5,1.5,Iris-versicolor
+5.7,2.8,4.5,1.3,Iris-versicolor
+5.6,3.0,4.1,1.3,Iris-versicolor
+5.7,2.3,3.9,1.2,Iris-versicolor
+5.7,3.0,4.1,1.3,Iris-versicolor
+6.1,2.9,4.6,1.4,Iris-versicolor
+5.9,3.1,4.3,1.5,Iris-versicolor
+5.5,2.4,3.8,1.1,Iris-versicolor
+5.1,2.1,3.6,1.0,Iris-versicolor
+5.3,2.4,3.6,1.0,Iris-versicolor
+6.5,2.9,4.5,1.3,Iris-versicolor
+5.7,2.7,4.1,1.3,Iris-versicolor
+5.4,2.4,3.8,1.1,Iris-versicolor
+6.1,2.8,4.6,1.2,Iris-versicolor
+6.2,2.8,4.5,1.3,Iris-versicolor
+5.8,2.8,3.9,1.3,Iris-versicolor
+6.8,2.8,4.8,1.4,Iris-versicolor
+5.4,3.0,4.5,1.4,Iris-versicolor
+6.0,2.2,4.1,1.1,Iris-versicolor
+6.1,2.9,4.8,1.4,Iris-versicolor
+5.4,3.0,4.5,1.5,Iris-versicolor
+5.6,3.0,3.8,1.3,Iris-versicolor
+6.5,2.8,4.6,1.4,Iris-versicolor
+6.0,2.9,4.5,1.5,Iris-versicolor
+6.5,2.8,4.6,1.4,Iris-versicolor
+6.1,3.0,4.6,1.4,Iris-versicolor
+5.6,3.0,4.1,1.3,Iris-versicolor
+6.4,3.2,4.5,1.5,Iris-versicolor
+6.0,2.7,5.2,1.6,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+6.8,2.8,4.8,1.4,Iris-versicolor
+5.8,2.6,3.8,1.1,Iris-versicolor
+5.8,2.6,3.9,1.2,Iris-versicolor
+6.3,2.5,4.8,1.5,Iris-versicolor
+5.8,2.6,4.1,1.2,Iris-versicolor
+5.5,2.5,3.9,1.3,Iris-versicolor
+6.1,2.9,4.6,1.4,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+6.3,2.3,4.5,1.3,Iris-versicolor
+6.6,2.9,4.6,1.3,Iris-versicolor
+5.6,3.0,4.5,1.5,Iris-versicolor
+5.5,2.5,3.9,1.3,Iris-versicolor
+6.7,3.1,4.6,1.5,Iris-versicolor
+5.5,2.4,3.8,1.0,Iris-versicolor
+6.9,3.1,4.8,1.5,Iris-versicolor
+5.3,2.5,3.4,1.2,Iris-versicolor
+6.2,3.0,4.5,1.4,Iris-versicolor
+5.9,3.2,4.8,1.8,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+5.5,2.4,3.8,1.0,Iris-versicolor
+5.5,2.5,4.3,1.3,Iris-versicolor
+5.6,3.0,4.1,1.3,Iris-versicolor
+5.8,2.6,3.8,1.1,Iris-versicolor
+6.3,3.3,4.6,1.6,Iris-versicolor
+6.0,3.2,4.8,1.7,Iris-versicolor
+5.8,2.7,4.1,1.0,Iris-versicolor
+5.7,2.9,3.8,1.3,Iris-versicolor
+6.1,2.2,4.1,1.2,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+6.8,3.1,4.8,1.5,Iris-versicolor
+5.6,3.0,4.1,1.4,Iris-versicolor
+6.1,2.9,4.6,1.4,Iris-versicolor
+5.0,2.3,3.2,1.0,Iris-versicolor
+5.8,2.6,3.9,1.1,Iris-versicolor
+6.6,2.9,4.6,1.4,Iris-versicolor
+5.5,2.6,4.5,1.2,Iris-versicolor
+6.8,3.1,4.6,1.4,Iris-versicolor
+5.6,2.9,3.6,1.3,Iris-versicolor
+5.8,2.3,4.1,1.3,Iris-versicolor
+5.8,2.7,3.9,1.0,Iris-versicolor
+6.2,2.9,4.3,1.3,Iris-versicolor
+5.7,3.0,4.1,1.3,Iris-versicolor
+6.0,2.2,4.1,1.1,Iris-versicolor
+6.7,3.1,4.5,1.4,Iris-versicolor
+5.5,3.0,4.3,1.4,Iris-versicolor
+5.8,3.0,4.3,1.5,Iris-versicolor
+6.0,2.9,4.3,1.4,Iris-versicolor
+5.8,2.8,3.8,1.3,Iris-versicolor
+7.0,3.2,4.6,1.4,Iris-versicolor
+5.7,2.7,4.1,1.3,Iris-versicolor
+6.1,2.9,4.6,1.4,Iris-versicolor
+5.0,2.4,3.4,1.0,Iris-versicolor
+5.5,2.4,3.8,1.1,Iris-versicolor
+4.9,2.4,3.2,1.0,Iris-versicolor
+6.4,2.9,4.3,1.4,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+5.4,3.0,4.5,1.5,Iris-versicolor
+5.7,2.9,4.3,1.3,Iris-versicolor
+5.7,2.9,4.1,1.3,Iris-versicolor
+5.5,2.6,4.5,1.2,Iris-versicolor
+6.7,3.0,5.0,1.7,Iris-versicolor
+5.7,2.7,4.1,1.3,Iris-versicolor
+5.6,2.9,3.8,1.3,Iris-versicolor
+5.8,2.6,3.6,1.0,Iris-versicolor
+5.8,2.7,4.1,1.0,Iris-versicolor
+5.5,3.0,4.3,1.4,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+6.2,2.9,4.3,1.3,Iris-versicolor
+5.8,2.6,3.9,1.1,Iris-versicolor
+6.0,2.9,4.5,1.4,Iris-versicolor
+6.8,3.1,4.6,1.4,Iris-versicolor
+7.0,3.2,4.8,1.4,Iris-versicolor
+6.1,2.8,4.6,1.2,Iris-versicolor
+6.1,2.3,4.3,1.3,Iris-versicolor
+5.4,3.0,4.5,1.5,Iris-versicolor
+6.1,3.0,4.6,1.4,Iris-versicolor
+6.3,2.2,4.5,1.4,Iris-versicolor
+6.9,3.2,4.6,1.4,Iris-versicolor
+6.4,2.6,4.8,1.5,Iris-versicolor
+5.6,2.5,3.6,1.0,Iris-versicolor
+5.2,2.4,3.6,1.0,Iris-versicolor
+5.0,2.0,3.6,1.0,Iris-versicolor
+6.1,2.9,4.8,1.4,Iris-versicolor
+5.9,3.2,4.8,1.8,Iris-versicolor
+6.1,2.8,4.1,1.4,Iris-versicolor
+5.5,2.4,3.6,1.0,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+5.7,3.1,4.6,1.7,Iris-versicolor
+5.8,2.6,3.9,1.1,Iris-versicolor
+5.3,2.7,3.9,1.4,Iris-versicolor
+6.6,3.1,4.6,1.5,Iris-versicolor
+6.1,2.8,4.8,1.5,Iris-versicolor
+5.8,2.8,3.9,1.3,Iris-versicolor
+5.6,3.0,4.3,1.4,Iris-versicolor
+6.9,3.1,4.8,1.4,Iris-versicolor
+5.6,2.9,3.6,1.3,Iris-versicolor
+6.0,3.4,4.5,1.6,Iris-versicolor
+5.6,2.4,3.8,1.0,Iris-versicolor
+6.0,2.9,4.6,1.5,Iris-versicolor
+6.1,2.9,4.6,1.4,Iris-versicolor
+6.5,3.1,4.6,1.5,Iris-versicolor
+5.6,3.0,4.1,1.3,Iris-versicolor
+6.2,2.9,4.3,1.4,Iris-versicolor
+5.7,2.9,4.3,1.4,Iris-versicolor
+5.7,2.7,4.1,1.2,Iris-versicolor
+6.3,3.3,4.6,1.6,Iris-versicolor
+6.0,2.3,4.3,1.3,Iris-versicolor
+6.8,3.0,5.0,1.7,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+5.8,3.0,4.3,1.4,Iris-versicolor
+6.0,2.9,4.5,1.4,Iris-versicolor
+5.7,2.9,4.1,1.3,Iris-versicolor
+6.0,3.2,4.8,1.7,Iris-versicolor
+6.4,2.8,4.5,1.4,Iris-versicolor
+5.5,2.4,3.9,1.2,Iris-versicolor
+5.3,2.2,3.6,1.0,Iris-versicolor
+6.0,3.4,4.5,1.6,Iris-versicolor
+5.8,2.6,4.1,1.2,Iris-versicolor
+5.8,2.6,3.8,1.1,Iris-versicolor
+5.7,3.0,4.1,1.3,Iris-versicolor
+6.1,2.8,4.1,1.3,Iris-versicolor
+5.6,2.3,3.9,1.3,Iris-versicolor
+6.3,2.9,4.3,1.3,Iris-versicolor
+5.7,2.7,4.1,1.3,Iris-versicolor
+5.9,3.0,4.3,1.5,Iris-versicolor
+6.2,2.2,4.5,1.5,Iris-versicolor
+5.4,2.4,3.6,1.0,Iris-versicolor
+6.8,3.1,4.6,1.4,Iris-versicolor
+5.9,3.0,4.3,1.5,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+5.7,2.7,4.1,1.3,Iris-versicolor
+6.7,3.0,5.0,1.7,Iris-versicolor
+5.8,3.0,4.1,1.4,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+5.7,2.7,4.1,1.3,Iris-versicolor
+5.9,3.2,4.8,1.7,Iris-versicolor
+5.6,3.0,4.5,1.5,Iris-versicolor
+5.5,3.0,4.5,1.5,Iris-versicolor
+6.1,2.8,4.1,1.3,Iris-versicolor
+5.6,3.0,4.1,1.3,Iris-versicolor
+5.2,2.3,3.4,1.0,Iris-versicolor
+6.7,3.1,4.6,1.5,Iris-versicolor
+5.7,2.9,4.1,1.3,Iris-versicolor
+6.7,3.1,4.6,1.5,Iris-versicolor
+5.6,3.0,4.5,1.5,Iris-versicolor
+5.5,2.4,3.8,1.1,Iris-versicolor
+5.8,2.6,3.8,1.0,Iris-versicolor
+5.7,2.9,4.1,1.3,Iris-versicolor
+5.6,3.0,4.5,1.5,Iris-versicolor
+5.4,2.4,3.8,1.1,Iris-versicolor
+5.7,2.7,4.1,1.3,Iris-versicolor
+5.7,2.6,3.6,1.0,Iris-versicolor
+6.7,3.0,5.0,1.7,Iris-versicolor
+6.8,3.1,4.8,1.5,Iris-versicolor
+4.9,2.4,3.2,1.0,Iris-versicolor
+5.6,2.5,3.8,1.0,Iris-versicolor
+6.2,3.0,4.5,1.4,Iris-versicolor
+5.3,2.4,3.4,1.1,Iris-versicolor
+6.7,3.1,4.6,1.4,Iris-versicolor
+6.3,2.3,4.6,1.5,Iris-versicolor
+5.7,2.9,4.1,1.3,Iris-versicolor
+5.6,3.0,4.5,1.5,Iris-versicolor
+6.8,3.1,4.8,1.5,Iris-versicolor
+5.7,2.9,4.3,1.3,Iris-versicolor
+5.4,3.0,4.5,1.5,Iris-versicolor
+6.2,3.0,4.5,1.4,Iris-versicolor
+6.7,3.1,4.6,1.5,Iris-versicolor
+6.2,2.2,4.5,1.5,Iris-versicolor
+6.1,3.0,4.5,1.4,Iris-versicolor
+6.1,2.9,4.6,1.4,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+6.0,2.9,4.3,1.4,Iris-versicolor
+5.9,3.2,4.8,1.8,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+5.5,2.3,3.9,1.3,Iris-versicolor
+5.1,2.4,3.1,1.0,Iris-versicolor
+6.3,2.4,4.6,1.4,Iris-versicolor
+5.7,2.9,4.3,1.4,Iris-versicolor
+6.3,3.3,4.6,1.6,Iris-versicolor
+6.6,2.9,4.6,1.4,Iris-versicolor
+5.3,2.4,3.6,1.0,Iris-versicolor
+6.3,2.9,4.3,1.3,Iris-versicolor
+5.7,2.5,3.9,1.3,Iris-versicolor
+4.9,2.2,3.4,1.0,Iris-versicolor
+5.5,2.3,3.9,1.3,Iris-versicolor
+5.0,2.0,3.6,1.0,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+5.5,2.4,3.9,1.2,Iris-versicolor
+6.5,2.8,4.6,1.4,Iris-versicolor
+6.1,2.8,4.1,1.3,Iris-versicolor
+5.5,2.3,3.9,1.3,Iris-versicolor
+6.4,3.2,4.5,1.5,Iris-versicolor
+5.8,2.6,3.9,1.2,Iris-versicolor
+5.0,2.1,3.4,1.0,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+6.3,3.3,4.6,1.6,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+5.6,3.0,4.5,1.5,Iris-versicolor
+5.5,2.4,3.8,1.1,Iris-versicolor
+5.7,2.8,4.3,1.3,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+6.5,3.1,4.5,1.4,Iris-versicolor
+6.1,2.2,4.1,1.2,Iris-versicolor
+5.4,3.0,4.5,1.5,Iris-versicolor
+6.1,2.9,4.6,1.4,Iris-versicolor
+6.1,2.9,4.6,1.4,Iris-versicolor
+5.6,2.5,3.8,1.0,Iris-versicolor
+5.3,2.6,3.9,1.4,Iris-versicolor
+4.9,2.3,3.4,1.0,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+5.7,2.8,4.3,1.3,Iris-versicolor
+5.8,2.6,3.6,1.0,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+5.5,2.4,3.8,1.0,Iris-versicolor
+5.3,2.7,3.9,1.4,Iris-versicolor
+6.1,2.8,4.6,1.3,Iris-versicolor
+5.7,2.8,3.6,1.3,Iris-versicolor
+6.6,2.9,4.8,1.6,Iris-versicolor
+6.5,3.1,4.5,1.4,Iris-versicolor
+5.9,2.7,5.0,1.6,Iris-versicolor
+6.8,3.1,5.0,1.7,Iris-versicolor
+5.5,2.4,3.9,1.2,Iris-versicolor
+6.3,2.9,4.3,1.3,Iris-versicolor
+5.5,2.4,3.9,1.1,Iris-versicolor
+6.0,3.4,4.5,1.6,Iris-versicolor
+5.3,2.2,3.6,1.0,Iris-versicolor
+6.0,3.4,4.5,1.6,Iris-versicolor
+5.6,2.9,3.8,1.3,Iris-versicolor
+5.7,2.9,4.3,1.4,Iris-versicolor
+6.7,3.0,5.0,1.7,Iris-versicolor
+6.7,2.8,4.6,1.4,Iris-versicolor
+6.6,2.9,4.6,1.4,Iris-versicolor
+5.6,2.5,3.6,1.0,Iris-versicolor
+6.7,3.1,4.5,1.4,Iris-versicolor
+6.5,2.9,4.3,1.4,Iris-versicolor
+6.1,2.9,4.6,1.4,Iris-versicolor
+6.0,2.7,5.0,1.6,Iris-versicolor
+6.0,2.9,4.5,1.5,Iris-versicolor
+5.7,2.8,4.3,1.3,Iris-versicolor
+5.1,2.1,3.6,1.0,Iris-versicolor
+6.1,2.2,4.1,1.1,Iris-versicolor
+5.5,2.4,3.9,1.1,Iris-versicolor
+5.7,2.5,3.8,1.0,Iris-versicolor
+5.3,2.4,3.6,1.0,Iris-versicolor
+6.2,2.9,4.3,1.4,Iris-versicolor
+6.2,3.3,4.6,1.7,Iris-versicolor
+6.4,2.8,4.5,1.4,Iris-versicolor
+6.5,3.2,4.5,1.5,Iris-versicolor
+6.7,3.1,4.5,1.4,Iris-versicolor
+5.8,3.0,4.5,1.5,Iris-versicolor
+6.1,2.8,4.1,1.3,Iris-versicolor
+5.5,2.4,3.8,1.0,Iris-versicolor
+5.8,2.7,4.1,1.0,Iris-versicolor
+6.1,3.0,4.6,1.4,Iris-versicolor
+5.5,2.4,3.9,1.2,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+6.2,2.9,4.3,1.3,Iris-versicolor
+5.9,3.0,4.3,1.5,Iris-versicolor
+6.7,3.1,4.6,1.5,Iris-versicolor
+5.7,2.7,4.1,1.2,Iris-versicolor
+5.5,2.6,4.3,1.2,Iris-versicolor
+6.5,2.9,4.3,1.4,Iris-versicolor
+6.6,3.1,4.6,1.5,Iris-versicolor
+5.9,3.1,4.3,1.6,Iris-versicolor
+6.3,2.9,4.5,1.4,Iris-versicolor
+5.3,2.4,3.4,1.0,Iris-versicolor
+6.1,2.9,4.6,1.4,Iris-versicolor
+5.3,2.2,3.6,1.0,Iris-versicolor
+5.1,2.1,3.6,1.0,Iris-versicolor
+5.9,3.1,4.5,1.7,Iris-versicolor
+5.5,2.3,3.9,1.2,Iris-versicolor
+5.7,2.5,3.8,1.0,Iris-versicolor
+6.4,3.2,4.5,1.5,Iris-versicolor
+5.7,2.8,4.5,1.3,Iris-versicolor
+5.8,2.7,3.8,1.1,Iris-versicolor
+6.1,2.9,4.6,1.4,Iris-versicolor
+5.4,2.4,3.6,1.0,Iris-versicolor
+5.6,2.9,3.6,1.3,Iris-versicolor
+6.7,3.1,4.6,1.5,Iris-versicolor
+5.8,2.6,3.8,1.1,Iris-versicolor
+6.6,2.9,4.6,1.3,Iris-versicolor
+5.9,3.2,4.8,1.8,Iris-versicolor
+6.1,2.3,4.3,1.3,Iris-versicolor
+5.4,2.5,3.8,1.3,Iris-versicolor
+6.9,3.1,4.8,1.5,Iris-versicolor
+5.8,2.7,4.1,1.0,Iris-versicolor
+5.8,3.0,4.3,1.5,Iris-versicolor
+6.5,2.9,4.5,1.3,Iris-versicolor
+5.8,2.3,4.1,1.3,Iris-versicolor
+6.8,3.1,4.6,1.4,Iris-versicolor
+6.6,3.1,4.5,1.4,Iris-versicolor
+6.1,2.8,4.6,1.2,Iris-versicolor
+6.1,2.4,4.3,1.3,Iris-versicolor
+5.7,2.7,4.1,1.3,Iris-versicolor
+5.6,2.6,4.3,1.2,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+5.7,2.5,3.6,1.0,Iris-versicolor
+6.5,3.1,4.5,1.4,Iris-versicolor
+6.0,2.2,3.9,1.0,Iris-versicolor
+6.0,2.9,4.5,1.4,Iris-versicolor
+6.1,2.9,4.6,1.4,Iris-versicolor
+6.7,2.9,4.6,1.4,Iris-versicolor
+6.1,2.9,4.6,1.4,Iris-versicolor
+5.3,2.4,3.6,1.0,Iris-versicolor
+6.7,3.1,4.6,1.5,Iris-versicolor
+6.1,2.3,4.3,1.3,Iris-versicolor
+5.7,3.0,4.3,1.4,Iris-versicolor
+5.7,3.0,4.5,1.4,Iris-versicolor
+6.7,3.0,4.5,1.4,Iris-versicolor
+6.2,3.3,4.5,1.6,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+5.8,2.7,4.1,1.0,Iris-versicolor
+5.4,2.4,3.6,1.0,Iris-versicolor
+5.8,2.6,3.9,1.2,Iris-versicolor
+4.9,2.2,3.4,1.0,Iris-versicolor
+6.3,3.3,4.6,1.6,Iris-versicolor
+6.6,2.8,4.5,1.4,Iris-versicolor
+6.8,2.8,4.8,1.4,Iris-versicolor
+5.5,2.5,3.9,1.3,Iris-versicolor
+5.8,2.8,4.1,1.3,Iris-versicolor
+5.6,2.9,4.3,1.4,Iris-versicolor
+5.6,3.0,4.1,1.3,Iris-versicolor
+6.6,3.1,4.6,1.5,Iris-versicolor
+5.7,3.0,4.5,1.4,Iris-versicolor
+6.9,3.1,4.8,1.5,Iris-versicolor
+5.0,2.3,3.2,1.0,Iris-versicolor
+6.9,3.2,4.6,1.4,Iris-versicolor
+6.2,3.3,4.5,1.6,Iris-versicolor
+5.0,2.4,3.4,1.0,Iris-versicolor
+5.7,2.7,4.1,1.3,Iris-versicolor
+5.8,2.8,3.9,1.2,Iris-versicolor
+5.5,2.4,3.8,1.0,Iris-versicolor
+6.1,2.8,4.1,1.3,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+6.0,3.4,4.5,1.6,Iris-versicolor
+5.8,2.6,3.9,1.2,Iris-versicolor
+6.0,2.7,5.0,1.6,Iris-versicolor
+6.7,3.1,4.5,1.4,Iris-versicolor
+6.8,2.8,4.8,1.4,Iris-versicolor
+6.2,2.2,4.5,1.4,Iris-versicolor
+6.1,2.8,3.9,1.3,Iris-versicolor
+5.8,2.6,3.9,1.2,Iris-versicolor
+5.7,2.7,4.1,1.1,Iris-versicolor
+6.3,2.3,4.5,1.3,Iris-versicolor
+5.0,2.3,3.4,1.0,Iris-versicolor
+5.8,2.6,3.9,1.2,Iris-versicolor
+5.7,2.8,4.3,1.3,Iris-versicolor
+5.5,2.4,3.9,1.3,Iris-versicolor
+6.0,2.2,4.1,1.1,Iris-versicolor
+5.7,2.9,4.1,1.3,Iris-versicolor
+6.8,2.8,4.8,1.4,Iris-versicolor
+6.1,3.0,4.6,1.4,Iris-versicolor
+5.7,3.0,4.5,1.4,Iris-versicolor
+5.9,3.2,4.8,1.7,Iris-versicolor
+6.0,3.4,4.5,1.6,Iris-versicolor
+6.2,2.2,4.5,1.5,Iris-versicolor
+5.7,2.9,3.8,1.3,Iris-versicolor
+5.5,3.0,4.5,1.4,Iris-versicolor
+6.8,2.8,4.8,1.4,Iris-versicolor
+5.6,2.9,3.6,1.3,Iris-versicolor
+6.1,2.6,5.0,1.6,Iris-versicolor
+6.9,3.1,4.8,1.5,Iris-versicolor
+5.4,2.5,3.8,1.3,Iris-versicolor
+5.7,2.5,3.9,1.1,Iris-versicolor
+6.4,2.6,4.8,1.5,Iris-versicolor
+5.4,3.0,4.5,1.5,Iris-versicolor
+5.1,2.5,3.1,1.1,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+5.6,3.0,4.5,1.5,Iris-versicolor
+5.1,2.4,3.1,1.0,Iris-versicolor
+5.0,2.1,3.4,1.0,Iris-versicolor
+5.5,2.4,3.9,1.3,Iris-versicolor
+6.3,2.5,4.8,1.5,Iris-versicolor
+5.7,3.0,4.3,1.2,Iris-versicolor
+6.2,2.9,4.6,1.4,Iris-versicolor
+5.6,3.0,4.1,1.3,Iris-versicolor
+6.1,2.3,4.3,1.3,Iris-versicolor
+6.0,2.9,4.6,1.5,Iris-versicolor
+6.7,3.0,5.0,1.7,Iris-versicolor
+6.7,3.1,4.6,1.5,Iris-versicolor
+5.7,2.8,3.6,1.3,Iris-versicolor
+5.6,2.5,3.6,1.0,Iris-versicolor
+6.3,2.4,4.6,1.4,Iris-versicolor
+6.8,3.1,5.0,1.6,Iris-versicolor
+5.7,2.7,4.1,1.2,Iris-versicolor
+5.8,2.7,3.9,1.1,Iris-versicolor
+6.1,2.3,4.3,1.3,Iris-versicolor
+5.8,2.3,4.1,1.3,Iris-versicolor
+5.7,3.0,3.9,1.3,Iris-versicolor
+5.8,3.0,4.1,1.4,Iris-versicolor
+5.4,2.4,3.6,1.0,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+5.9,3.0,4.3,1.5,Iris-versicolor
+5.3,2.4,3.6,1.0,Iris-versicolor
+7.0,3.1,4.8,1.4,Iris-versicolor
+5.8,2.8,4.1,1.3,Iris-versicolor
+5.4,3.0,4.5,1.5,Iris-versicolor
+6.9,3.1,4.8,1.5,Iris-versicolor
+5.7,2.6,3.9,1.2,Iris-versicolor
+5.8,2.7,4.1,1.0,Iris-versicolor
+5.1,2.5,3.1,1.1,Iris-versicolor
+6.9,3.2,4.6,1.4,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+5.8,2.6,3.9,1.2,Iris-versicolor
+6.9,3.1,4.8,1.5,Iris-versicolor
+6.1,3.0,4.6,1.4,Iris-versicolor
+5.5,2.4,3.9,1.1,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+6.4,3.2,4.5,1.5,Iris-versicolor
+5.8,2.6,4.1,1.2,Iris-versicolor
+6.1,2.9,4.8,1.4,Iris-versicolor
+6.1,2.8,4.6,1.2,Iris-versicolor
+6.9,3.1,4.8,1.4,Iris-versicolor
+5.6,2.9,4.3,1.4,Iris-versicolor
+6.5,2.8,4.5,1.4,Iris-versicolor
+6.0,2.4,4.3,1.4,Iris-versicolor
+5.7,2.9,4.3,1.3,Iris-versicolor
+5.7,2.5,3.9,1.3,Iris-versicolor
+6.1,2.8,4.6,1.2,Iris-versicolor
+5.6,3.0,4.5,1.5,Iris-versicolor
+6.0,2.2,3.9,1.0,Iris-versicolor
+6.7,3.1,4.5,1.4,Iris-versicolor
+5.6,3.0,4.3,1.4,Iris-versicolor
+5.5,2.4,3.8,1.1,Iris-versicolor
+5.0,2.4,3.1,1.0,Iris-versicolor
+6.6,2.9,4.6,1.3,Iris-versicolor
+5.6,3.0,4.1,1.3,Iris-versicolor
+6.3,2.5,4.8,1.5,Iris-versicolor
+6.2,2.2,4.5,1.5,Iris-versicolor
+6.2,2.6,5.0,1.5,Iris-versicolor
+6.2,2.9,4.3,1.3,Iris-versicolor
+6.3,2.9,4.3,1.3,Iris-versicolor
+6.7,3.1,4.5,1.4,Iris-versicolor
+6.0,2.8,3.9,1.3,Iris-versicolor
+5.8,2.8,4.1,1.1,Iris-versicolor
+5.8,2.6,4.1,1.2,Iris-versicolor
+5.7,2.9,4.3,1.4,Iris-versicolor
+5.6,3.0,4.1,1.3,Iris-versicolor
+6.0,2.8,4.8,1.6,Iris-versicolor
+5.5,2.4,3.9,1.3,Iris-versicolor
+6.0,3.4,4.5,1.6,Iris-versicolor
+6.7,3.1,4.6,1.5,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+6.3,2.2,4.5,1.4,Iris-versicolor
+6.0,2.2,4.1,1.1,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+6.1,3.4,4.5,1.6,Iris-versicolor
+6.0,2.2,4.1,1.0,Iris-versicolor
+5.7,2.9,4.3,1.3,Iris-versicolor
+6.7,3.1,4.5,1.4,Iris-versicolor
+5.4,3.0,4.5,1.5,Iris-versicolor
+6.9,3.1,4.6,1.4,Iris-versicolor
+5.4,2.4,3.8,1.1,Iris-versicolor
+6.7,3.0,4.5,1.4,Iris-versicolor
+5.6,2.4,3.9,1.1,Iris-versicolor
+5.5,2.4,3.8,1.1,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+6.7,2.9,4.6,1.4,Iris-versicolor
+6.1,2.8,4.6,1.2,Iris-versicolor
+5.9,3.1,4.6,1.7,Iris-versicolor
+6.4,3.2,4.6,1.6,Iris-versicolor
+5.8,2.2,4.3,1.4,Iris-versicolor
+5.6,3.0,4.1,1.3,Iris-versicolor
+6.7,3.1,4.6,1.5,Iris-versicolor
+6.3,2.4,4.8,1.5,Iris-versicolor
+5.8,2.7,4.1,1.0,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+5.8,2.6,3.9,1.2,Iris-versicolor
+6.5,2.9,4.3,1.4,Iris-versicolor
+5.7,2.7,4.1,1.3,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+6.2,2.2,4.5,1.5,Iris-versicolor
+6.3,2.3,4.5,1.3,Iris-versicolor
+6.7,3.0,5.0,1.7,Iris-versicolor
+6.8,3.1,4.6,1.4,Iris-versicolor
+6.1,2.6,5.0,1.6,Iris-versicolor
+5.0,2.0,3.6,1.0,Iris-versicolor
+5.7,3.0,3.9,1.3,Iris-versicolor
+5.8,2.7,3.9,1.1,Iris-versicolor
+6.1,2.2,4.1,1.2,Iris-versicolor
+5.5,2.3,3.9,1.2,Iris-versicolor
+5.8,3.0,4.1,1.4,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+5.4,2.4,3.6,1.1,Iris-versicolor
+6.2,3.3,4.8,1.7,Iris-versicolor
+5.8,2.7,4.1,1.1,Iris-versicolor
+5.8,2.6,3.8,1.1,Iris-versicolor
+6.9,3.1,4.8,1.5,Iris-versicolor
+6.5,3.1,4.5,1.4,Iris-versicolor
+5.5,2.4,3.8,1.1,Iris-versicolor
+5.7,2.9,4.3,1.3,Iris-versicolor
+6.3,2.9,4.3,1.3,Iris-versicolor
+6.0,3.2,4.8,1.7,Iris-versicolor
+5.8,3.0,4.5,1.5,Iris-versicolor
+6.4,2.8,4.5,1.4,Iris-versicolor
+6.6,3.0,4.5,1.4,Iris-versicolor
+6.0,2.2,3.9,1.0,Iris-versicolor
+5.5,2.6,4.3,1.2,Iris-versicolor
+6.1,2.9,4.5,1.4,Iris-versicolor
+6.8,2.8,4.8,1.4,Iris-versicolor
+5.7,2.7,4.1,1.1,Iris-versicolor
+5.4,2.5,3.8,1.3,Iris-versicolor
+6.9,3.1,5.0,1.5,Iris-versicolor
+6.6,3.1,4.5,1.4,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+6.4,2.6,4.8,1.5,Iris-versicolor
+5.6,2.5,3.6,1.0,Iris-versicolor
+6.5,2.8,4.6,1.4,Iris-versicolor
+6.3,2.4,4.6,1.5,Iris-versicolor
+5.1,2.1,3.6,1.0,Iris-versicolor
+5.6,2.5,3.6,1.0,Iris-versicolor
+6.2,3.3,4.8,1.7,Iris-versicolor
+6.8,3.1,5.0,1.6,Iris-versicolor
+5.1,2.4,3.1,1.0,Iris-versicolor
+6.3,2.9,4.5,1.3,Iris-versicolor
+6.3,2.3,4.3,1.3,Iris-versicolor
+5.6,3.0,4.3,1.4,Iris-versicolor
+5.8,2.3,3.9,1.0,Iris-versicolor
+5.5,2.4,3.9,1.2,Iris-versicolor
+6.5,2.8,4.6,1.4,Iris-versicolor
+6.0,2.7,5.2,1.6,Iris-versicolor
+5.5,2.7,4.1,1.4,Iris-versicolor
+6.1,2.4,4.3,1.3,Iris-versicolor
+5.7,3.0,4.3,1.3,Iris-versicolor
+6.0,2.3,4.3,1.3,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+5.0,2.4,3.2,1.0,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+5.7,2.3,3.9,1.2,Iris-versicolor
+6.8,3.1,4.8,1.5,Iris-versicolor
+6.8,3.1,4.6,1.4,Iris-versicolor
+5.7,3.0,4.1,1.2,Iris-versicolor
+5.7,2.8,4.5,1.3,Iris-versicolor
+6.6,2.9,4.5,1.4,Iris-versicolor
+5.9,2.2,3.9,1.0,Iris-versicolor
+5.5,2.4,3.8,1.1,Iris-versicolor
+6.2,2.9,4.3,1.4,Iris-versicolor
+5.5,2.3,3.9,1.3,Iris-versicolor
+5.5,2.4,4.1,1.3,Iris-versicolor
+5.6,2.5,3.8,1.0,Iris-versicolor
+5.7,3.0,4.5,1.4,Iris-versicolor
+4.9,2.3,3.4,1.0,Iris-versicolor
+6.1,2.2,4.1,1.1,Iris-versicolor
+5.5,2.3,3.9,1.2,Iris-versicolor
+6.8,3.0,5.0,1.7,Iris-versicolor
+6.6,3.0,4.5,1.4,Iris-versicolor
+6.1,2.9,4.6,1.4,Iris-versicolor
+6.3,3.3,4.6,1.6,Iris-versicolor
+6.3,2.9,4.3,1.3,Iris-versicolor
+4.9,2.4,3.2,1.0,Iris-versicolor
+5.1,2.4,3.1,1.0,Iris-versicolor
+5.5,2.4,4.1,1.3,Iris-versicolor
+5.0,2.1,3.4,1.0,Iris-versicolor
+5.6,2.4,3.9,1.1,Iris-versicolor
+5.7,2.9,4.3,1.3,Iris-versicolor
+6.2,2.9,4.5,1.4,Iris-versicolor
+5.8,2.6,3.9,1.2,Iris-versicolor
+5.6,3.0,4.5,1.5,Iris-versicolor
+5.6,2.9,3.6,1.3,Iris-versicolor
+6.8,2.8,4.8,1.5,Iris-versicolor
+5.6,2.9,3.6,1.3,Iris-versicolor
+5.7,3.1,4.6,1.7,Iris-versicolor
+6.2,3.3,4.8,1.7,Iris-versicolor
+6.3,2.8,4.5,1.3,Iris-versicolor
+5.8,2.6,4.1,1.2,Iris-versicolor
+5.5,2.4,3.8,1.0,Iris-versicolor
+6.6,3.0,4.5,1.4,Iris-versicolor
+6.4,2.9,4.3,1.4,Iris-versicolor
+6.1,2.9,4.6,1.4,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+6.6,3.0,4.5,1.4,Iris-versicolor
+6.3,2.5,4.8,1.5,Iris-versicolor
+6.8,2.8,4.8,1.4,Iris-versicolor
+5.1,2.5,3.1,1.1,Iris-versicolor
+5.8,2.6,3.9,1.2,Iris-versicolor
+6.6,2.9,4.8,1.6,Iris-versicolor
+5.0,2.1,3.4,1.0,Iris-versicolor
+5.7,3.0,4.3,1.3,Iris-versicolor
+5.5,2.4,3.8,1.1,Iris-versicolor
+5.7,3.0,4.3,1.2,Iris-versicolor
+6.1,2.8,4.6,1.2,Iris-versicolor
+6.8,2.8,4.8,1.4,Iris-versicolor
+5.4,2.5,3.8,1.3,Iris-versicolor
+5.7,2.9,4.3,1.4,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+5.5,2.4,3.6,1.0,Iris-versicolor
+6.5,2.8,4.5,1.5,Iris-versicolor
+5.7,2.5,3.9,1.1,Iris-versicolor
+6.7,3.1,4.5,1.4,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+5.2,2.3,3.6,1.0,Iris-versicolor
+6.2,2.6,5.0,1.5,Iris-versicolor
+5.0,2.3,3.2,1.0,Iris-versicolor
+6.3,2.4,4.6,1.4,Iris-versicolor
+6.2,2.9,4.3,1.3,Iris-versicolor
+6.2,3.3,4.5,1.6,Iris-versicolor
+5.8,2.6,3.8,1.1,Iris-versicolor
+6.3,3.3,4.6,1.6,Iris-versicolor
+6.0,2.9,4.5,1.4,Iris-versicolor
+6.2,3.0,4.5,1.4,Iris-versicolor
+5.8,2.3,3.9,1.0,Iris-versicolor
+5.8,2.7,4.1,1.0,Iris-versicolor
+5.5,2.4,3.8,1.1,Iris-versicolor
+5.8,2.6,3.9,1.1,Iris-versicolor
+5.6,3.0,4.3,1.3,Iris-versicolor
+6.7,3.1,4.5,1.4,Iris-versicolor
+6.6,2.8,4.5,1.4,Iris-versicolor
+6.5,3.1,4.6,1.5,Iris-versicolor
+6.2,3.0,4.5,1.4,Iris-versicolor
+5.0,2.0,3.6,1.0,Iris-versicolor
+6.6,2.9,4.6,1.4,Iris-versicolor
+6.0,3.4,4.5,1.7,Iris-versicolor
+6.8,2.9,4.8,1.6,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+6.3,2.4,4.8,1.4,Iris-versicolor
+6.6,3.1,4.5,1.4,Iris-versicolor
+5.8,2.6,3.9,1.2,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+5.1,2.4,3.1,1.0,Iris-versicolor
+5.4,2.4,3.8,1.0,Iris-versicolor
+5.9,3.0,4.3,1.5,Iris-versicolor
+5.8,2.7,3.9,1.1,Iris-versicolor
+6.8,3.1,5.0,1.6,Iris-versicolor
+6.5,2.9,4.5,1.3,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+5.7,2.7,4.1,1.3,Iris-versicolor
+6.0,3.4,4.5,1.7,Iris-versicolor
+5.4,3.0,4.5,1.5,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+5.7,3.0,4.5,1.5,Iris-versicolor
+5.8,3.0,4.3,1.4,Iris-versicolor
+6.0,2.2,4.1,1.1,Iris-versicolor
+5.6,3.0,4.5,1.5,Iris-versicolor
+6.8,3.1,4.8,1.5,Iris-versicolor
+5.7,3.0,3.9,1.3,Iris-versicolor
+5.7,2.3,4.1,1.3,Iris-versicolor
+5.5,2.6,4.3,1.3,Iris-versicolor
+5.7,2.7,4.1,1.2,Iris-versicolor
+5.6,2.7,4.1,1.3,Iris-versicolor
+6.0,2.8,4.8,1.6,Iris-versicolor
+6.2,3.3,4.8,1.7,Iris-versicolor
+6.3,2.4,4.8,1.5,Iris-versicolor
+6.1,2.2,4.1,1.1,Iris-versicolor
+6.4,3.2,4.5,1.5,Iris-versicolor
+6.6,2.9,4.6,1.4,Iris-versicolor
+6.3,2.9,4.3,1.3,Iris-versicolor
+6.3,2.4,4.8,1.5,Iris-versicolor
+6.8,3.1,4.5,1.4,Iris-versicolor
+5.7,2.5,3.6,1.0,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+5.7,3.0,4.5,1.4,Iris-versicolor
+6.6,2.9,4.6,1.4,Iris-versicolor
+6.1,2.4,4.3,1.3,Iris-versicolor
+5.8,3.2,4.8,1.7,Iris-versicolor
+6.5,2.9,4.5,1.3,Iris-versicolor
+6.3,2.3,4.3,1.3,Iris-versicolor
+6.1,2.4,4.3,1.3,Iris-versicolor
+5.4,3.0,4.5,1.5,Iris-versicolor
+5.2,2.4,3.6,1.0,Iris-versicolor
+5.6,2.9,3.8,1.3,Iris-versicolor
+6.1,2.3,4.3,1.3,Iris-versicolor
+6.3,3.3,4.6,1.6,Iris-versicolor
+5.7,2.9,4.1,1.3,Iris-versicolor
+5.5,2.4,3.8,1.1,Iris-versicolor
+6.0,2.3,4.3,1.3,Iris-versicolor
+6.2,3.0,4.5,1.4,Iris-versicolor
+6.9,3.1,4.8,1.4,Iris-versicolor
+6.7,3.1,4.6,1.5,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+6.7,3.1,4.6,1.5,Iris-versicolor
+5.8,2.7,4.1,1.0,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+6.1,2.8,4.6,1.2,Iris-versicolor
+5.8,2.6,4.1,1.2,Iris-versicolor
+5.5,2.3,3.9,1.3,Iris-versicolor
+5.6,3.0,4.1,1.4,Iris-versicolor
+6.3,2.9,4.3,1.3,Iris-versicolor
+5.5,2.6,4.3,1.2,Iris-versicolor
+5.9,3.2,4.8,1.7,Iris-versicolor
+5.6,2.7,4.3,1.3,Iris-versicolor
+5.7,2.5,3.8,1.0,Iris-versicolor
+5.8,2.6,3.8,1.1,Iris-versicolor
+5.7,3.0,4.1,1.3,Iris-versicolor
+5.8,2.8,3.8,1.2,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+6.5,2.9,4.3,1.4,Iris-versicolor
+5.4,2.4,3.8,1.0,Iris-versicolor
+6.1,2.8,4.1,1.3,Iris-versicolor
+6.2,2.2,4.5,1.5,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+6.6,3.0,4.5,1.4,Iris-versicolor
+6.8,3.1,4.8,1.5,Iris-versicolor
+5.1,2.5,3.1,1.1,Iris-versicolor
+5.9,3.2,4.8,1.8,Iris-versicolor
+6.6,2.8,4.6,1.4,Iris-versicolor
+5.5,2.3,3.9,1.2,Iris-versicolor
+5.7,2.8,4.5,1.3,Iris-versicolor
+5.8,2.8,3.9,1.3,Iris-versicolor
+5.7,2.7,4.1,1.3,Iris-versicolor
+6.1,2.8,4.6,1.3,Iris-versicolor
+6.9,3.2,4.6,1.4,Iris-versicolor
+5.8,2.7,3.9,1.0,Iris-versicolor
+6.3,3.3,4.6,1.6,Iris-versicolor
+6.3,2.3,4.5,1.3,Iris-versicolor
+5.6,3.0,4.1,1.3,Iris-versicolor
+6.1,2.8,4.6,1.2,Iris-versicolor
+5.3,2.2,3.6,1.0,Iris-versicolor
+5.7,3.0,4.1,1.2,Iris-versicolor
+5.6,2.4,3.9,1.1,Iris-versicolor
+6.1,2.3,4.3,1.3,Iris-versicolor
+6.8,3.1,4.8,1.5,Iris-versicolor
+6.4,2.9,4.3,1.3,Iris-versicolor
+6.1,2.3,4.3,1.3,Iris-versicolor
+5.6,3.0,4.3,1.3,Iris-versicolor
+6.7,3.1,4.8,1.7,Iris-versicolor
+5.2,2.4,3.4,1.0,Iris-versicolor
+5.3,2.4,3.6,1.0,Iris-versicolor
+5.5,2.6,4.3,1.2,Iris-versicolor
+5.3,2.2,3.6,1.0,Iris-versicolor
+5.7,2.8,4.3,1.3,Iris-versicolor
+5.6,3.0,4.1,1.3,Iris-versicolor
+6.8,3.0,5.0,1.7,Iris-versicolor
+6.1,3.0,4.6,1.4,Iris-versicolor
+6.1,3.0,4.6,1.4,Iris-versicolor
+6.3,2.9,4.3,1.3,Iris-versicolor
+6.7,3.1,4.5,1.4,Iris-versicolor
+6.6,3.1,4.6,1.5,Iris-versicolor
+5.7,3.0,4.3,1.2,Iris-versicolor
+6.0,2.7,5.2,1.6,Iris-versicolor
+6.1,3.0,4.6,1.4,Iris-versicolor
+4.9,2.2,3.4,1.0,Iris-versicolor
+4.9,2.4,3.2,1.0,Iris-versicolor
+5.5,2.3,3.9,1.2,Iris-versicolor
+6.9,3.1,4.8,1.5,Iris-versicolor
+5.8,2.6,3.9,1.2,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+6.1,2.4,4.3,1.3,Iris-versicolor
+6.2,2.7,4.8,1.4,Iris-versicolor
+6.6,2.9,4.6,1.3,Iris-versicolor
+5.8,2.7,3.9,1.0,Iris-versicolor
+5.8,2.6,3.9,1.2,Iris-versicolor
+5.7,3.0,4.1,1.2,Iris-versicolor
+5.0,2.4,3.2,1.0,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+5.0,2.3,3.4,1.0,Iris-versicolor
+4.9,2.4,3.2,1.0,Iris-versicolor
+6.4,2.9,4.3,1.4,Iris-versicolor
+6.1,2.8,4.6,1.2,Iris-versicolor
+6.3,2.4,4.8,1.4,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+5.6,3.0,4.3,1.4,Iris-versicolor
+5.7,2.7,4.1,1.2,Iris-versicolor
+5.8,2.8,3.9,1.3,Iris-versicolor
+6.0,2.7,5.2,1.6,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+6.4,2.9,4.5,1.4,Iris-versicolor
+6.0,2.9,4.5,1.5,Iris-versicolor
+6.2,3.0,4.5,1.4,Iris-versicolor
+6.5,3.1,4.5,1.4,Iris-versicolor
+5.7,2.9,3.8,1.3,Iris-versicolor
+5.8,3.0,4.3,1.5,Iris-versicolor
+5.6,2.7,4.3,1.3,Iris-versicolor
+5.8,2.6,3.9,1.2,Iris-versicolor
+6.3,2.4,4.8,1.4,Iris-versicolor
+6.5,2.9,4.3,1.4,Iris-versicolor
+5.5,2.5,4.1,1.3,Iris-versicolor
+6.9,3.1,5.0,1.5,Iris-versicolor
+5.5,2.4,3.9,1.1,Iris-versicolor
+6.3,2.3,4.6,1.5,Iris-versicolor
+5.6,3.0,4.1,1.4,Iris-versicolor
+5.6,2.5,3.6,1.0,Iris-versicolor
+6.4,3.2,4.6,1.5,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+6.6,2.9,4.6,1.3,Iris-versicolor
+5.5,2.4,3.8,1.0,Iris-versicolor
+6.5,2.9,4.3,1.4,Iris-versicolor
+6.4,2.6,4.8,1.5,Iris-versicolor
+5.1,2.5,3.1,1.1,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+5.7,2.9,4.1,1.3,Iris-versicolor
+5.6,3.0,4.5,1.5,Iris-versicolor
+5.8,2.8,4.6,1.4,Iris-versicolor
+6.4,2.9,4.5,1.4,Iris-versicolor
+6.0,3.4,4.5,1.6,Iris-versicolor
+5.8,2.6,3.9,1.2,Iris-versicolor
+5.6,3.0,4.1,1.3,Iris-versicolor
+6.3,2.4,4.6,1.4,Iris-versicolor
+6.0,2.9,4.5,1.4,Iris-versicolor
+6.2,2.2,4.1,1.1,Iris-versicolor
+6.1,2.8,4.6,1.2,Iris-versicolor
+6.0,3.4,4.5,1.7,Iris-versicolor
+5.0,2.1,3.4,1.0,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+6.5,2.8,4.6,1.4,Iris-versicolor
+6.5,2.9,4.3,1.4,Iris-versicolor
+5.6,2.6,4.3,1.2,Iris-versicolor
+6.2,3.0,4.5,1.4,Iris-versicolor
+6.1,2.6,5.0,1.6,Iris-versicolor
+6.8,3.0,5.0,1.7,Iris-versicolor
+6.1,2.8,4.6,1.3,Iris-versicolor
+6.7,3.1,4.5,1.4,Iris-versicolor
+7.0,3.2,4.6,1.4,Iris-versicolor
+4.9,2.2,3.4,1.0,Iris-versicolor
+5.7,2.6,3.6,1.0,Iris-versicolor
+5.2,2.3,3.4,1.0,Iris-versicolor
+6.7,3.1,4.6,1.5,Iris-versicolor
+6.3,3.3,4.6,1.6,Iris-versicolor
+5.4,2.4,3.6,1.0,Iris-versicolor
+5.5,2.4,3.9,1.3,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+6.2,2.6,5.0,1.5,Iris-versicolor
+5.8,2.8,4.5,1.4,Iris-versicolor
+6.7,3.1,4.5,1.4,Iris-versicolor
+5.0,2.3,3.4,1.0,Iris-versicolor
+6.8,3.1,5.0,1.6,Iris-versicolor
+5.4,3.0,4.5,1.5,Iris-versicolor
+6.5,2.8,4.6,1.4,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+6.7,3.1,4.6,1.5,Iris-versicolor
+6.1,2.9,4.6,1.4,Iris-versicolor
+6.2,2.2,4.1,1.1,Iris-versicolor
+6.1,2.6,5.0,1.6,Iris-versicolor
+5.0,2.4,3.1,1.0,Iris-versicolor
+6.7,2.9,4.6,1.4,Iris-versicolor
+6.1,2.2,4.1,1.0,Iris-versicolor
+6.2,2.2,4.5,1.5,Iris-versicolor
+6.1,2.2,4.3,1.3,Iris-versicolor
+5.7,2.8,4.3,1.3,Iris-versicolor
+6.1,2.9,4.6,1.4,Iris-versicolor
+6.8,3.1,5.0,1.7,Iris-versicolor
+6.1,2.4,4.3,1.3,Iris-versicolor
+6.7,2.9,4.6,1.4,Iris-versicolor
+5.4,2.5,3.8,1.3,Iris-versicolor
+5.6,2.5,3.6,1.0,Iris-versicolor
+5.8,3.1,4.6,1.7,Iris-versicolor
+5.5,2.5,3.9,1.3,Iris-versicolor
+5.7,2.9,4.3,1.3,Iris-versicolor
+5.9,3.2,4.8,1.8,Iris-versicolor
+5.7,2.7,4.1,1.3,Iris-versicolor
+5.8,2.6,3.9,1.2,Iris-versicolor
+6.9,3.1,4.6,1.4,Iris-versicolor
+6.5,2.9,4.3,1.4,Iris-versicolor
+5.5,2.7,4.1,1.4,Iris-versicolor
+6.2,2.9,4.6,1.4,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+6.2,2.2,4.5,1.5,Iris-versicolor
+6.8,3.1,4.8,1.5,Iris-versicolor
+6.6,3.0,4.5,1.4,Iris-versicolor
+5.4,3.0,4.5,1.5,Iris-versicolor
+5.9,3.0,4.3,1.5,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+5.9,3.0,4.3,1.5,Iris-versicolor
+6.7,3.1,4.8,1.6,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+6.1,2.8,4.1,1.4,Iris-versicolor
+5.7,2.9,4.3,1.3,Iris-versicolor
+5.8,2.8,4.1,1.1,Iris-versicolor
+6.1,2.8,3.9,1.3,Iris-versicolor
+6.2,2.9,4.6,1.4,Iris-versicolor
+4.9,2.4,3.2,1.0,Iris-versicolor
+5.3,2.2,3.8,1.2,Iris-versicolor
+5.2,2.3,3.4,1.0,Iris-versicolor
+6.1,2.4,4.3,1.3,Iris-versicolor
+5.8,3.0,4.3,1.4,Iris-versicolor
+5.8,3.0,4.3,1.5,Iris-versicolor
+5.8,2.2,4.3,1.4,Iris-versicolor
+5.7,3.0,4.1,1.3,Iris-versicolor
+6.8,3.1,4.8,1.5,Iris-versicolor
+5.5,2.6,4.3,1.3,Iris-versicolor
+6.9,3.2,4.6,1.4,Iris-versicolor
+6.5,2.9,4.5,1.3,Iris-versicolor
+5.5,2.6,4.5,1.2,Iris-versicolor
+6.7,3.1,4.6,1.5,Iris-versicolor
+6.2,2.9,4.3,1.4,Iris-versicolor
+6.1,2.9,4.8,1.4,Iris-versicolor
+5.3,2.6,3.9,1.4,Iris-versicolor
+6.7,2.9,4.6,1.4,Iris-versicolor
+5.6,2.8,4.1,1.3,Iris-versicolor
+5.6,2.9,3.8,1.3,Iris-versicolor
+6.7,3.1,4.5,1.4,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+6.1,2.9,4.6,1.4,Iris-versicolor
+6.9,3.2,4.6,1.4,Iris-versicolor
+6.0,3.4,4.5,1.6,Iris-versicolor
+5.5,2.5,4.1,1.3,Iris-versicolor
+5.8,2.7,3.9,1.1,Iris-versicolor
+5.5,2.4,3.9,1.3,Iris-versicolor
+6.4,2.6,4.8,1.5,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+6.0,2.7,5.2,1.6,Iris-versicolor
+6.0,3.4,4.5,1.6,Iris-versicolor
+6.7,3.1,4.5,1.4,Iris-versicolor
+5.4,3.0,4.5,1.5,Iris-versicolor
+5.0,2.4,3.4,1.0,Iris-versicolor
+5.0,2.4,3.1,1.0,Iris-versicolor
+6.0,2.8,3.9,1.3,Iris-versicolor
+6.7,3.1,4.6,1.4,Iris-versicolor
+5.2,2.7,3.9,1.4,Iris-versicolor
+5.8,2.8,4.5,1.4,Iris-versicolor
+5.9,3.2,4.8,1.7,Iris-versicolor
+5.9,3.1,4.3,1.5,Iris-versicolor
+5.7,3.0,4.1,1.2,Iris-versicolor
+6.6,3.3,5.7,2.1,Iris-virginica
+6.3,2.8,5.5,2.1,Iris-virginica
+6.7,3.1,5.5,2.3,Iris-virginica
+6.4,3.4,5.5,2.4,Iris-virginica
+6.6,3.0,5.5,2.3,Iris-virginica
+6.5,3.0,5.2,2.0,Iris-virginica
+6.5,3.3,5.5,2.2,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6.5,3.1,5.2,2.0,Iris-virginica
+6.0,3.0,5.3,1.8,Iris-virginica
+7.2,2.9,6.0,1.7,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6.7,3.1,5.5,2.3,Iris-virginica
+7.2,3.6,6.0,2.5,Iris-virginica
+5.8,2.6,5.0,2.1,Iris-virginica
+6.5,3.2,5.2,2.1,Iris-virginica
+6.4,2.8,5.3,2.0,Iris-virginica
+6.5,3.0,5.5,2.1,Iris-virginica
+5.8,2.7,5.2,1.9,Iris-virginica
+6.8,3.2,5.7,2.2,Iris-virginica
+7.6,2.8,6.4,2.0,Iris-virginica
+6.2,3.4,5.5,2.3,Iris-virginica
+7.2,3.0,5.9,1.6,Iris-virginica
+6.8,3.0,5.2,2.3,Iris-virginica
+6.3,3.4,5.7,2.4,Iris-virginica
+7.3,2.9,6.2,1.8,Iris-virginica
+7.0,3.1,5.7,2.1,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6.3,2.7,5.2,1.8,Iris-virginica
+6.6,3.0,5.5,2.0,Iris-virginica
+6.0,2.9,5.0,1.8,Iris-virginica
+6.4,2.8,5.5,2.1,Iris-virginica
+6.1,2.2,5.0,1.6,Iris-virginica
+5.6,2.8,4.8,2.0,Iris-virginica
+6.3,2.6,5.0,1.9,Iris-virginica
+6.7,3.3,5.7,2.2,Iris-virginica
+6.4,2.8,5.7,2.2,Iris-virginica
+5.9,3.0,5.2,1.8,Iris-virginica
+7.6,3.0,6.6,2.1,Iris-virginica
+6.9,3.1,5.5,2.2,Iris-virginica
+7.6,3.5,6.2,1.9,Iris-virginica
+7.7,2.6,6.9,2.3,Iris-virginica
+6.8,3.1,5.5,2.4,Iris-virginica
+6.3,3.1,5.2,1.8,Iris-virginica
+7.2,2.9,6.0,1.7,Iris-virginica
+6.0,3.0,4.8,1.8,Iris-virginica
+6.3,2.9,5.3,1.8,Iris-virginica
+6.2,2.8,4.8,1.8,Iris-virginica
+6.4,2.8,5.7,2.1,Iris-virginica
+6.2,2.8,4.8,1.8,Iris-virginica
+5.9,3.0,5.0,1.8,Iris-virginica
+6.4,2.8,5.7,2.2,Iris-virginica
+6.1,2.6,5.5,1.4,Iris-virginica
+6.3,2.4,5.0,1.8,Iris-virginica
+6.4,2.8,5.5,2.2,Iris-virginica
+5.0,2.4,4.6,1.7,Iris-virginica
+5.8,2.7,5.2,1.9,Iris-virginica
+6.8,3.1,5.3,2.2,Iris-virginica
+7.2,3.0,6.0,2.0,Iris-virginica
+7.5,2.8,6.2,2.0,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+6.4,3.2,5.2,2.2,Iris-virginica
+6.5,3.0,5.5,1.8,Iris-virginica
+7.6,3.0,6.6,2.0,Iris-virginica
+6.7,2.5,5.7,1.8,Iris-virginica
+6.6,3.0,5.7,2.2,Iris-virginica
+6.5,2.5,5.3,1.9,Iris-virginica
+5.7,2.5,5.0,2.0,Iris-virginica
+7.5,2.8,6.0,2.0,Iris-virginica
+7.3,2.8,6.0,2.0,Iris-virginica
+6.5,3.1,5.2,2.0,Iris-virginica
+7.5,3.0,6.6,2.0,Iris-virginica
+6.8,3.3,5.7,2.2,Iris-virginica
+6.5,3.3,5.9,2.5,Iris-virginica
+6.9,3.1,5.5,2.1,Iris-virginica
+6.7,3.0,5.2,2.3,Iris-virginica
+6.3,2.7,5.2,1.8,Iris-virginica
+6.4,2.9,5.7,2.2,Iris-virginica
+6.2,2.8,4.8,1.8,Iris-virginica
+6.2,2.6,5.3,1.5,Iris-virginica
+7.7,2.9,6.7,2.1,Iris-virginica
+6.3,2.7,5.0,1.7,Iris-virginica
+6.1,2.6,5.5,1.4,Iris-virginica
+5.8,2.8,5.2,2.2,Iris-virginica
+6.6,3.0,5.7,2.2,Iris-virginica
+6.4,2.8,5.5,2.1,Iris-virginica
+6.4,2.7,5.2,1.8,Iris-virginica
+6.4,2.8,5.3,2.0,Iris-virginica
+6.7,2.5,5.7,1.8,Iris-virginica
+5.8,2.7,5.2,1.9,Iris-virginica
+6.3,2.8,5.2,1.5,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+6.2,2.8,4.8,1.8,Iris-virginica
+6.1,2.7,5.0,1.9,Iris-virginica
+6.7,2.5,5.9,1.8,Iris-virginica
+6.5,3.0,5.2,2.0,Iris-virginica
+6.8,3.1,5.2,2.3,Iris-virginica
+7.6,3.0,6.6,2.1,Iris-virginica
+5.9,2.5,5.0,2.0,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6.7,2.5,5.7,1.8,Iris-virginica
+6.2,2.8,4.8,1.8,Iris-virginica
+6.8,3.1,5.3,2.1,Iris-virginica
+6.3,3.4,5.7,2.3,Iris-virginica
+7.3,2.8,6.2,1.9,Iris-virginica
+7.1,3.0,5.9,2.1,Iris-virginica
+6.4,3.4,5.5,2.4,Iris-virginica
+6.8,3.1,5.3,2.3,Iris-virginica
+5.7,2.7,5.0,1.9,Iris-virginica
+6.1,2.3,5.0,1.6,Iris-virginica
+7.3,2.9,6.2,1.8,Iris-virginica
+6.9,3.1,5.2,2.2,Iris-virginica
+6.6,2.5,5.5,1.8,Iris-virginica
+7.2,3.1,5.9,1.7,Iris-virginica
+4.9,2.5,4.5,1.7,Iris-virginica
+5.6,2.7,5.0,1.9,Iris-virginica
+7.7,3.8,6.7,2.2,Iris-virginica
+5.9,2.5,5.0,2.0,Iris-virginica
+7.7,2.8,6.7,2.2,Iris-virginica
+6.4,2.8,5.5,2.1,Iris-virginica
+6.3,3.4,5.9,2.4,Iris-virginica
+6.3,2.7,5.0,1.7,Iris-virginica
+6.4,2.8,5.5,2.2,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+7.2,3.1,6.0,1.7,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+6.7,3.1,5.5,2.4,Iris-virginica
+7.2,3.1,6.0,1.9,Iris-virginica
+5.8,2.8,5.2,2.4,Iris-virginica
+6.3,3.1,5.2,1.8,Iris-virginica
+7.7,3.7,6.6,2.1,Iris-virginica
+6.4,2.8,5.7,2.2,Iris-virginica
+6.5,2.5,5.3,1.9,Iris-virginica
+7.7,2.9,6.7,2.1,Iris-virginica
+7.2,3.0,5.9,1.6,Iris-virginica
+6.2,2.7,5.2,1.7,Iris-virginica
+6.4,2.7,5.3,1.9,Iris-virginica
+6.4,2.9,5.5,2.0,Iris-virginica
+6.2,2.8,5.3,2.2,Iris-virginica
+6.3,3.3,6.0,2.5,Iris-virginica
+6.7,3.3,5.7,2.3,Iris-virginica
+5.8,2.6,5.0,2.0,Iris-virginica
+7.4,2.9,6.4,1.9,Iris-virginica
+6.5,3.0,5.5,1.8,Iris-virginica
+7.2,2.9,6.2,1.7,Iris-virginica
+7.2,3.0,6.2,1.8,Iris-virginica
+6.3,2.8,5.5,2.1,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+7.7,2.9,6.7,2.0,Iris-virginica
+6.1,3.0,5.0,1.8,Iris-virginica
+6.7,3.1,5.5,2.4,Iris-virginica
+6.1,3.0,4.8,1.8,Iris-virginica
+6.3,2.9,5.5,1.9,Iris-virginica
+5.7,2.8,5.0,2.0,Iris-virginica
+7.2,3.1,5.9,1.7,Iris-virginica
+6.4,2.7,5.3,1.9,Iris-virginica
+6.2,3.1,5.2,1.8,Iris-virginica
+6.4,2.8,5.5,2.2,Iris-virginica
+6.5,3.0,5.2,2.0,Iris-virginica
+7.2,3.1,5.9,1.7,Iris-virginica
+6.4,2.8,5.7,2.1,Iris-virginica
+7.2,2.9,6.2,2.0,Iris-virginica
+6.3,3.3,5.9,2.5,Iris-virginica
+6.7,3.0,5.2,2.3,Iris-virginica
+6.3,3.1,5.3,1.8,Iris-virginica
+6.3,3.1,5.5,1.8,Iris-virginica
+6.4,2.8,5.3,2.0,Iris-virginica
+6.5,3.0,5.5,1.8,Iris-virginica
+6.4,3.3,5.9,2.5,Iris-virginica
+7.5,2.8,6.6,1.9,Iris-virginica
+7.4,3.6,6.2,2.0,Iris-virginica
+6.8,3.1,5.7,2.2,Iris-virginica
+6.5,3.2,5.2,2.0,Iris-virginica
+6.3,2.7,5.0,1.7,Iris-virginica
+5.7,2.7,5.0,1.9,Iris-virginica
+6.5,3.0,5.2,2.0,Iris-virginica
+6.4,3.2,5.3,2.3,Iris-virginica
+6.5,3.0,5.9,2.2,Iris-virginica
+6.9,3.1,5.3,2.3,Iris-virginica
+7.2,3.1,5.9,1.7,Iris-virginica
+7.2,3.6,6.2,2.4,Iris-virginica
+6.4,3.4,5.7,2.3,Iris-virginica
+6.3,2.8,5.0,1.7,Iris-virginica
+6.1,2.9,5.0,1.8,Iris-virginica
+6.5,3.0,5.3,1.9,Iris-virginica
+7.0,3.1,5.7,2.1,Iris-virginica
+5.8,2.8,5.2,2.4,Iris-virginica
+7.7,3.0,6.0,2.3,Iris-virginica
+7.3,2.9,6.0,1.8,Iris-virginica
+6.4,2.8,5.5,2.1,Iris-virginica
+5.7,2.5,5.0,2.0,Iris-virginica
+6.5,3.4,5.7,2.3,Iris-virginica
+6.3,2.5,5.0,1.9,Iris-virginica
+6.5,3.0,5.9,2.2,Iris-virginica
+6.8,3.0,5.5,2.0,Iris-virginica
+6.6,3.1,5.7,2.3,Iris-virginica
+7.7,3.0,6.4,2.1,Iris-virginica
+6.3,3.1,5.3,1.8,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+6.5,3.0,5.9,2.2,Iris-virginica
+6.8,3.1,5.3,2.2,Iris-virginica
+6.4,2.8,5.7,2.1,Iris-virginica
+6.1,3.0,5.0,1.8,Iris-virginica
+5.6,2.7,5.0,1.9,Iris-virginica
+6.6,3.0,5.5,2.3,Iris-virginica
+7.2,2.9,6.0,2.0,Iris-virginica
+6.6,3.2,5.3,2.0,Iris-virginica
+7.7,3.0,6.0,2.3,Iris-virginica
+7.3,2.9,6.4,1.8,Iris-virginica
+6.0,2.7,5.0,1.9,Iris-virginica
+6.5,2.5,5.3,1.9,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+6.6,3.0,5.7,2.2,Iris-virginica
+6.5,3.0,5.2,2.0,Iris-virginica
+6.3,2.8,5.5,2.0,Iris-virginica
+6.4,2.8,5.3,2.0,Iris-virginica
+7.7,2.6,6.9,2.3,Iris-virginica
+6.8,3.3,5.7,2.1,Iris-virginica
+7.2,2.9,6.2,2.0,Iris-virginica
+6.3,2.7,5.2,1.9,Iris-virginica
+6.5,3.0,5.2,2.0,Iris-virginica
+6.3,2.8,5.5,2.0,Iris-virginica
+6.1,3.0,4.8,1.8,Iris-virginica
+6.3,2.7,5.2,1.9,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+6.5,3.3,5.9,2.5,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+6.2,2.8,4.8,1.8,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+5.7,2.8,5.0,2.0,Iris-virginica
+6.0,3.0,4.8,1.8,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+7.0,3.1,5.5,2.0,Iris-virginica
+6.1,3.0,5.0,1.8,Iris-virginica
+6.3,2.9,5.3,1.8,Iris-virginica
+6.0,2.6,5.3,1.6,Iris-virginica
+6.3,2.5,5.0,1.9,Iris-virginica
+7.2,3.0,5.9,1.8,Iris-virginica
+6.0,3.0,5.0,1.8,Iris-virginica
+6.2,2.8,4.8,1.8,Iris-virginica
+6.8,3.1,5.3,2.1,Iris-virginica
+6.6,3.3,5.9,2.5,Iris-virginica
+7.2,3.0,5.9,1.7,Iris-virginica
+6.2,3.0,5.0,1.8,Iris-virginica
+7.2,3.0,6.0,2.0,Iris-virginica
+7.3,2.9,6.2,1.8,Iris-virginica
+6.1,2.4,5.0,1.6,Iris-virginica
+6.8,3.1,5.3,2.3,Iris-virginica
+6.4,3.4,5.5,2.2,Iris-virginica
+6.3,3.3,5.5,2.3,Iris-virginica
+7.7,2.7,6.7,2.2,Iris-virginica
+6.3,2.6,5.0,1.9,Iris-virginica
+6.0,2.6,5.3,1.6,Iris-virginica
+7.2,3.0,6.0,2.0,Iris-virginica
+7.7,2.9,6.7,2.0,Iris-virginica
+6.3,3.1,5.5,1.8,Iris-virginica
+6.5,3.0,5.5,2.1,Iris-virginica
+6.3,3.4,5.7,2.4,Iris-virginica
+6.7,3.0,5.2,2.3,Iris-virginica
+6.8,3.1,5.3,2.3,Iris-virginica
+6.4,3.1,5.3,1.9,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+6.4,2.9,5.5,2.0,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+7.4,2.8,6.0,1.9,Iris-virginica
+6.3,2.9,5.5,1.8,Iris-virginica
+6.8,3.1,5.7,2.3,Iris-virginica
+7.7,2.9,6.6,2.0,Iris-virginica
+6.7,3.0,5.5,2.0,Iris-virginica
+7.7,3.0,6.4,2.1,Iris-virginica
+6.6,2.5,5.7,1.7,Iris-virginica
+6.8,3.1,5.5,2.2,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+7.0,3.1,5.7,2.1,Iris-virginica
+6.4,3.1,5.3,1.9,Iris-virginica
+6.3,2.9,5.5,1.8,Iris-virginica
+6.5,3.2,5.2,2.0,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+7.5,2.9,6.2,2.0,Iris-virginica
+6.6,3.0,5.7,2.2,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+6.3,2.9,5.5,1.8,Iris-virginica
+5.8,2.7,5.2,1.9,Iris-virginica
+7.2,3.2,6.0,1.8,Iris-virginica
+6.8,3.3,5.9,2.4,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+6.0,2.2,5.0,1.5,Iris-virginica
+6.8,3.1,5.7,2.4,Iris-virginica
+5.9,3.0,5.0,1.8,Iris-virginica
+6.0,2.6,5.3,1.6,Iris-virginica
+6.3,2.5,5.0,1.9,Iris-virginica
+6.8,3.3,5.7,2.4,Iris-virginica
+7.3,2.9,6.4,1.8,Iris-virginica
+5.8,2.6,5.0,2.0,Iris-virginica
+7.7,2.8,6.7,2.2,Iris-virginica
+6.3,3.1,5.5,1.8,Iris-virginica
+5.9,2.5,5.0,2.0,Iris-virginica
+6.4,3.1,5.3,1.9,Iris-virginica
+6.1,2.4,5.0,1.6,Iris-virginica
+7.1,3.4,5.9,2.4,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6.0,2.2,5.0,1.5,Iris-virginica
+6.3,2.8,5.5,2.1,Iris-virginica
+7.4,3.0,6.4,2.0,Iris-virginica
+7.3,3.4,6.2,1.9,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+7.3,2.8,6.2,1.9,Iris-virginica
+7.2,3.6,6.2,2.4,Iris-virginica
+6.3,2.8,4.8,1.8,Iris-virginica
+5.9,2.8,5.2,2.3,Iris-virginica
+6.3,3.1,5.2,1.8,Iris-virginica
+6.0,2.8,5.0,1.9,Iris-virginica
+6.3,2.8,5.2,1.5,Iris-virginica
+6.7,3.2,5.9,2.3,Iris-virginica
+7.7,3.3,6.6,2.0,Iris-virginica
+5.8,3.0,5.0,1.9,Iris-virginica
+7.6,3.5,6.2,1.9,Iris-virginica
+7.7,3.0,6.0,2.3,Iris-virginica
+6.6,3.3,5.5,2.0,Iris-virginica
+7.7,3.0,6.0,2.3,Iris-virginica
+5.7,2.8,5.0,2.0,Iris-virginica
+6.7,3.0,5.5,2.0,Iris-virginica
+6.1,3.0,4.8,1.8,Iris-virginica
+6.3,2.8,4.8,1.8,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+7.2,3.5,6.0,2.0,Iris-virginica
+5.8,2.2,5.0,1.5,Iris-virginica
+7.2,3.0,6.2,1.8,Iris-virginica
+6.4,2.8,5.5,2.2,Iris-virginica
+6.3,2.8,5.0,1.6,Iris-virginica
+6.4,2.8,5.5,2.1,Iris-virginica
+6.8,3.3,5.7,2.4,Iris-virginica
+6.5,3.1,5.2,2.0,Iris-virginica
+6.8,3.1,5.5,2.1,Iris-virginica
+7.6,3.5,6.2,1.9,Iris-virginica
+7.2,3.0,6.0,1.7,Iris-virginica
+6.8,3.3,5.9,2.2,Iris-virginica
+6.5,3.0,5.9,2.2,Iris-virginica
+5.1,2.5,4.6,1.7,Iris-virginica
+6.6,3.0,5.5,2.3,Iris-virginica
+5.8,2.7,5.2,2.0,Iris-virginica
+7.2,3.1,5.9,1.7,Iris-virginica
+6.3,2.8,5.0,1.8,Iris-virginica
+5.7,2.8,5.0,2.0,Iris-virginica
+7.3,2.9,6.2,1.8,Iris-virginica
+7.7,3.8,6.7,2.2,Iris-virginica
+6.4,2.8,5.3,2.0,Iris-virginica
+7.5,3.4,6.2,1.9,Iris-virginica
+7.0,3.3,5.9,2.3,Iris-virginica
+6.5,3.0,5.9,2.2,Iris-virginica
+6.3,2.7,5.2,1.9,Iris-virginica
+7.0,3.1,5.5,2.0,Iris-virginica
+6.6,3.1,5.7,2.3,Iris-virginica
+6.7,3.1,5.5,2.4,Iris-virginica
+7.2,3.0,6.2,1.8,Iris-virginica
+6.4,3.4,5.7,2.3,Iris-virginica
+6.9,3.1,5.5,2.2,Iris-virginica
+5.8,2.6,5.0,2.2,Iris-virginica
+6.3,3.3,5.3,2.3,Iris-virginica
+6.4,2.8,5.5,2.2,Iris-virginica
+6.6,3.3,5.5,2.0,Iris-virginica
+6.5,3.1,5.2,2.0,Iris-virginica
+6.9,3.1,5.5,2.2,Iris-virginica
+7.5,2.9,6.4,2.0,Iris-virginica
+7.2,3.0,6.0,1.7,Iris-virginica
+5.8,2.7,5.2,1.9,Iris-virginica
+7.7,3.0,6.6,2.1,Iris-virginica
+6.4,2.9,5.5,2.1,Iris-virginica
+6.4,3.1,5.3,1.9,Iris-virginica
+6.8,3.3,5.7,2.1,Iris-virginica
+6.3,2.9,5.5,1.8,Iris-virginica
+6.3,3.4,5.5,2.4,Iris-virginica
+6.0,2.7,5.0,1.9,Iris-virginica
+6.7,2.5,5.9,1.8,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+6.3,3.1,5.3,1.8,Iris-virginica
+6.6,3.3,5.7,2.5,Iris-virginica
+7.1,3.0,5.9,2.1,Iris-virginica
+6.4,2.8,5.3,2.0,Iris-virginica
+5.8,2.7,5.2,1.9,Iris-virginica
+6.5,2.5,5.3,1.9,Iris-virginica
+6.2,3.0,5.2,1.8,Iris-virginica
+7.2,3.1,5.9,2.0,Iris-virginica
+6.1,2.8,4.8,1.8,Iris-virginica
+6.3,2.7,5.0,1.8,Iris-virginica
+6.3,3.0,5.5,1.8,Iris-virginica
+6.6,3.3,5.9,2.5,Iris-virginica
+6.8,3.1,5.3,2.3,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+5.7,2.5,5.0,2.0,Iris-virginica
+7.1,3.4,5.9,2.0,Iris-virginica
+6.0,2.8,5.0,1.9,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+6.4,2.8,5.7,2.1,Iris-virginica
+6.4,3.1,5.3,1.9,Iris-virginica
+6.3,2.7,4.8,1.8,Iris-virginica
+6.3,2.7,5.0,1.8,Iris-virginica
+6.8,3.1,5.5,2.2,Iris-virginica
+7.0,3.1,5.5,2.0,Iris-virginica
+6.3,3.1,5.3,1.8,Iris-virginica
+6.7,3.1,5.3,2.3,Iris-virginica
+6.7,3.1,5.5,2.3,Iris-virginica
+6.3,2.6,5.7,1.6,Iris-virginica
+7.5,3.0,6.0,2.2,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+7.7,3.8,6.7,2.2,Iris-virginica
+6.8,3.3,5.7,2.1,Iris-virginica
+6.7,2.5,5.9,1.8,Iris-virginica
+7.5,2.8,6.6,1.9,Iris-virginica
+4.9,2.5,4.5,1.7,Iris-virginica
+6.2,2.9,4.8,1.8,Iris-virginica
+6.5,3.0,5.3,2.0,Iris-virginica
+6.8,3.1,5.3,2.1,Iris-virginica
+6.0,2.9,5.0,1.8,Iris-virginica
+6.3,2.7,5.3,1.7,Iris-virginica
+6.0,3.0,4.8,1.8,Iris-virginica
+6.4,3.4,5.7,2.3,Iris-virginica
+6.6,3.0,5.5,2.0,Iris-virginica
+7.1,3.0,5.9,2.1,Iris-virginica
+7.4,2.8,6.2,1.9,Iris-virginica
+6.3,3.3,5.9,2.5,Iris-virginica
+7.5,3.0,6.0,2.2,Iris-virginica
+7.4,2.9,6.4,1.9,Iris-virginica
+6.3,2.9,5.5,1.9,Iris-virginica
+6.4,2.9,5.7,2.2,Iris-virginica
+7.3,2.8,6.2,1.9,Iris-virginica
+5.7,2.8,5.0,2.0,Iris-virginica
+6.4,2.8,5.5,2.2,Iris-virginica
+7.7,3.0,6.0,2.3,Iris-virginica
+6.4,2.8,5.5,2.1,Iris-virginica
+6.3,2.8,5.0,1.6,Iris-virginica
+6.4,3.2,5.2,2.2,Iris-virginica
+6.2,2.8,4.8,1.8,Iris-virginica
+7.7,2.7,6.7,2.3,Iris-virginica
+5.6,2.8,4.8,2.0,Iris-virginica
+6.4,2.8,5.5,2.1,Iris-virginica
+7.2,3.6,6.2,2.2,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+5.8,2.7,5.0,1.9,Iris-virginica
+5.9,3.0,5.0,1.8,Iris-virginica
+6.8,3.1,5.3,2.2,Iris-virginica
+7.7,2.9,6.7,2.1,Iris-virginica
+6.0,3.0,5.0,1.8,Iris-virginica
+6.2,2.8,4.8,1.8,Iris-virginica
+6.9,3.1,5.2,2.2,Iris-virginica
+6.6,3.0,5.5,2.0,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+7.7,3.0,6.0,2.3,Iris-virginica
+7.7,2.6,6.9,2.3,Iris-virginica
+5.9,2.5,5.0,2.0,Iris-virginica
+6.2,2.6,5.5,1.6,Iris-virginica
+6.5,3.0,5.2,2.0,Iris-virginica
+6.3,2.9,5.5,1.8,Iris-virginica
+7.5,2.8,6.2,2.0,Iris-virginica
+7.1,3.0,5.9,2.1,Iris-virginica
+6.6,3.3,5.7,2.5,Iris-virginica
+6.3,2.3,5.3,1.7,Iris-virginica
+7.5,2.8,6.6,1.9,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6.2,3.4,5.3,2.3,Iris-virginica
+6.8,3.2,5.9,2.4,Iris-virginica
+6.5,3.0,5.5,1.8,Iris-virginica
+6.8,3.1,5.7,2.3,Iris-virginica
+6.8,3.3,5.9,2.4,Iris-virginica
+6.8,3.1,5.3,2.1,Iris-virginica
+6.5,3.0,5.2,2.0,Iris-virginica
+6.7,3.0,5.2,2.3,Iris-virginica
+5.8,2.6,5.0,2.1,Iris-virginica
+6.7,2.5,5.7,1.8,Iris-virginica
+6.3,3.1,5.3,1.8,Iris-virginica
+6.4,2.8,5.5,2.1,Iris-virginica
+6.2,3.0,5.0,1.8,Iris-virginica
+6.3,3.4,5.5,2.4,Iris-virginica
+5.9,2.8,5.2,2.3,Iris-virginica
+7.2,3.5,6.0,2.0,Iris-virginica
+7.7,2.9,6.6,2.0,Iris-virginica
+6.0,3.0,5.0,1.8,Iris-virginica
+7.2,3.5,6.2,2.1,Iris-virginica
+7.8,3.8,6.6,2.1,Iris-virginica
+6.3,3.1,5.3,1.8,Iris-virginica
+6.3,3.1,5.2,1.8,Iris-virginica
+6.1,2.8,5.3,2.3,Iris-virginica
+6.3,2.6,5.2,1.9,Iris-virginica
+6.3,3.2,5.3,2.3,Iris-virginica
+6.3,2.6,5.0,1.9,Iris-virginica
+6.3,2.8,5.5,2.0,Iris-virginica
+7.5,3.0,6.6,2.0,Iris-virginica
+6.3,3.4,5.9,2.5,Iris-virginica
+6.0,2.9,5.0,1.8,Iris-virginica
+6.1,2.6,5.5,1.4,Iris-virginica
+7.1,3.0,5.9,2.1,Iris-virginica
+7.1,3.0,5.7,2.1,Iris-virginica
+7.7,3.8,6.7,2.2,Iris-virginica
+6.8,3.1,5.7,2.4,Iris-virginica
+6.3,3.3,5.9,2.5,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+6.5,2.4,5.5,1.7,Iris-virginica
+7.1,3.0,5.9,2.1,Iris-virginica
+6.4,2.8,5.3,2.0,Iris-virginica
+7.3,2.8,6.0,2.0,Iris-virginica
+6.4,2.8,5.5,2.1,Iris-virginica
+5.8,2.7,5.2,1.9,Iris-virginica
+6.7,3.3,5.7,2.2,Iris-virginica
+6.3,2.7,5.0,1.7,Iris-virginica
+6.2,2.5,5.0,1.9,Iris-virginica
+6.3,3.4,5.7,2.4,Iris-virginica
+6.3,2.7,5.3,1.7,Iris-virginica
+6.7,3.3,5.7,2.4,Iris-virginica
+7.5,2.8,6.6,2.0,Iris-virginica
+7.7,2.9,6.7,2.0,Iris-virginica
+7.0,3.1,5.7,2.1,Iris-virginica
+6.3,2.7,5.0,1.8,Iris-virginica
+6.2,2.8,4.8,1.8,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+7.2,3.1,5.9,1.7,Iris-virginica
+6.5,3.0,5.3,1.9,Iris-virginica
+7.4,2.8,6.0,1.9,Iris-virginica
+6.3,3.4,5.5,2.3,Iris-virginica
+6.7,2.5,5.9,1.8,Iris-virginica
+6.0,3.0,4.8,1.8,Iris-virginica
+6.5,3.0,5.2,2.0,Iris-virginica
+7.7,2.6,6.9,2.3,Iris-virginica
+6.3,2.8,5.3,1.7,Iris-virginica
+6.4,2.8,5.7,2.2,Iris-virginica
+5.8,3.0,5.0,1.8,Iris-virginica
+6.3,3.3,5.3,2.3,Iris-virginica
+5.7,2.8,5.0,2.2,Iris-virginica
+7.5,3.6,6.4,2.3,Iris-virginica
+7.3,2.9,6.4,1.8,Iris-virginica
+6.8,3.0,5.5,2.0,Iris-virginica
+6.0,3.0,4.8,1.8,Iris-virginica
+6.3,2.8,5.0,1.7,Iris-virginica
+6.3,2.8,5.5,2.0,Iris-virginica
+7.7,3.0,6.0,2.3,Iris-virginica
+6.5,3.2,5.2,2.1,Iris-virginica
+6.0,2.2,5.0,1.5,Iris-virginica
+6.5,3.0,5.5,1.8,Iris-virginica
+7.7,2.9,6.7,2.1,Iris-virginica
+6.8,3.2,5.9,2.4,Iris-virginica
+7.6,3.0,6.0,2.3,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+6.9,3.1,5.2,2.3,Iris-virginica
+6.4,3.2,5.2,2.2,Iris-virginica
+6.3,3.1,5.3,1.8,Iris-virginica
+6.3,3.3,5.9,2.5,Iris-virginica
+6.9,3.1,5.5,2.1,Iris-virginica
+6.3,3.3,5.3,2.3,Iris-virginica
+6.4,3.0,5.3,2.0,Iris-virginica
+7.4,2.8,6.2,1.9,Iris-virginica
+6.1,2.3,5.0,1.6,Iris-virginica
+6.6,2.6,5.7,1.9,Iris-virginica
+6.2,2.5,5.0,1.9,Iris-virginica
+6.9,3.2,5.5,2.3,Iris-virginica
+5.1,2.5,4.6,1.7,Iris-virginica
+7.7,2.8,6.6,2.3,Iris-virginica
+6.3,2.5,5.0,1.9,Iris-virginica
+6.3,2.9,5.3,1.8,Iris-virginica
+7.5,2.7,6.4,2.0,Iris-virginica
+6.4,2.8,5.3,2.0,Iris-virginica
+6.1,2.2,5.0,1.6,Iris-virginica
+6.5,2.4,5.5,1.7,Iris-virginica
+7.2,3.0,6.2,1.8,Iris-virginica
+6.7,3.3,5.7,2.3,Iris-virginica
+7.4,2.8,6.0,1.9,Iris-virginica
+7.7,2.6,6.9,2.3,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6.0,3.0,4.8,1.8,Iris-virginica
+7.2,3.1,5.9,2.0,Iris-virginica
+7.5,2.8,6.4,2.0,Iris-virginica
+6.5,3.2,5.2,2.0,Iris-virginica
+6.1,2.8,5.3,2.3,Iris-virginica
+7.8,3.8,6.6,2.1,Iris-virginica
+7.7,2.8,6.7,2.2,Iris-virginica
+7.5,2.9,6.4,2.0,Iris-virginica
+6.3,2.7,5.2,1.9,Iris-virginica
+6.5,3.0,5.9,2.2,Iris-virginica
+6.6,3.3,5.7,2.1,Iris-virginica
+5.3,2.6,4.8,1.8,Iris-virginica
+7.5,3.6,6.4,2.3,Iris-virginica
+6.0,2.7,5.0,1.9,Iris-virginica
+7.0,3.1,5.7,2.1,Iris-virginica
+6.0,2.8,5.0,1.9,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+6.3,2.5,5.0,1.9,Iris-virginica
+5.8,3.0,5.0,1.8,Iris-virginica
+6.8,3.2,5.7,2.3,Iris-virginica
+6.3,2.8,5.0,1.8,Iris-virginica
+6.6,3.0,5.5,2.3,Iris-virginica
+7.2,3.2,6.0,1.8,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+6.8,3.1,5.7,2.4,Iris-virginica
+6.4,2.9,5.7,2.1,Iris-virginica
+6.2,2.8,4.8,1.8,Iris-virginica
+7.2,3.6,6.2,2.2,Iris-virginica
+6.6,3.3,5.7,2.5,Iris-virginica
+5.9,3.0,5.2,1.8,Iris-virginica
+6.1,2.8,4.8,1.8,Iris-virginica
+7.9,3.8,6.4,2.0,Iris-virginica
+7.5,2.8,6.6,1.9,Iris-virginica
+7.1,3.1,5.7,1.8,Iris-virginica
+6.6,3.0,5.7,2.2,Iris-virginica
+7.7,2.7,6.7,2.1,Iris-virginica
+6.5,3.1,5.2,2.0,Iris-virginica
+7.4,3.6,6.0,2.0,Iris-virginica
+5.3,2.6,4.6,1.8,Iris-virginica
+7.0,3.1,5.9,2.2,Iris-virginica
+6.8,3.1,5.7,2.3,Iris-virginica
+6.3,2.8,5.3,1.8,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6.5,3.0,5.3,1.9,Iris-virginica
+6.9,3.0,5.7,2.1,Iris-virginica
+7.5,3.4,6.2,1.9,Iris-virginica
+6.6,3.0,5.7,2.2,Iris-virginica
+6.3,2.8,5.2,1.5,Iris-virginica
+7.7,3.8,6.6,2.3,Iris-virginica
+6.3,3.0,5.5,1.8,Iris-virginica
+6.8,3.1,5.2,2.3,Iris-virginica
+6.3,3.1,5.3,1.8,Iris-virginica
+7.3,2.9,6.4,1.8,Iris-virginica
+6.3,3.3,5.9,2.5,Iris-virginica
+6.4,2.8,5.5,2.2,Iris-virginica
+6.6,3.0,5.7,2.2,Iris-virginica
+6.3,3.4,5.7,2.4,Iris-virginica
+7.2,3.1,6.0,2.0,Iris-virginica
+6.3,2.8,5.5,2.3,Iris-virginica
+7.7,3.0,6.6,2.1,Iris-virginica
+6.2,2.9,4.8,1.8,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+7.4,2.8,6.2,1.9,Iris-virginica
+6.5,3.0,5.7,2.2,Iris-virginica
+7.6,2.9,6.4,2.0,Iris-virginica
+6.3,2.9,5.5,1.8,Iris-virginica
+7.4,3.7,6.2,2.3,Iris-virginica
+6.7,3.1,5.5,2.3,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6.8,3.1,5.5,2.1,Iris-virginica
+7.3,3.3,6.0,1.8,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+7.2,3.2,6.0,1.8,Iris-virginica
+6.3,2.8,4.8,1.8,Iris-virginica
+6.8,3.1,5.7,2.4,Iris-virginica
+6.5,3.0,5.2,2.0,Iris-virginica
+6.3,3.4,5.5,2.4,Iris-virginica
+6.8,3.1,5.7,2.4,Iris-virginica
+6.3,2.9,5.5,1.8,Iris-virginica
+6.8,3.3,5.9,2.4,Iris-virginica
+7.6,2.8,6.4,2.0,Iris-virginica
+6.3,2.5,5.0,1.9,Iris-virginica
+6.8,3.1,5.5,2.3,Iris-virginica
+6.4,2.8,5.5,2.1,Iris-virginica
+6.2,2.8,4.8,1.8,Iris-virginica
+6.3,2.9,5.5,1.8,Iris-virginica
+6.2,2.8,4.8,1.8,Iris-virginica
+5.8,2.8,5.2,2.4,Iris-virginica
+6.9,3.1,5.3,2.3,Iris-virginica
+5.3,2.6,4.6,1.8,Iris-virginica
+6.3,2.8,5.3,1.8,Iris-virginica
+6.7,3.3,5.7,2.2,Iris-virginica
+7.3,2.9,6.2,1.8,Iris-virginica
+7.2,3.2,6.0,1.8,Iris-virginica
+7.2,3.0,6.2,1.8,Iris-virginica
+6.4,3.1,5.3,1.9,Iris-virginica
+6.5,3.0,5.9,2.2,Iris-virginica
+6.5,3.2,5.2,2.0,Iris-virginica
+7.7,2.8,6.7,2.0,Iris-virginica
+6.4,2.8,5.7,2.0,Iris-virginica
+6.2,3.4,5.3,2.3,Iris-virginica
+6.4,2.8,5.5,2.1,Iris-virginica
+6.3,3.1,5.2,1.8,Iris-virginica
+6.5,3.2,5.2,2.1,Iris-virginica
+6.8,3.1,5.2,2.3,Iris-virginica
+7.1,3.0,5.9,2.1,Iris-virginica
+6.7,3.0,5.2,2.3,Iris-virginica
+6.7,3.0,5.2,2.2,Iris-virginica
+6.4,2.8,5.7,2.0,Iris-virginica
+6.6,3.3,5.7,2.5,Iris-virginica
+6.7,3.0,5.5,2.0,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+6.4,3.2,5.3,2.3,Iris-virginica
+7.0,3.2,5.9,2.2,Iris-virginica
+6.3,2.3,5.3,1.7,Iris-virginica
+6.4,3.2,5.3,2.3,Iris-virginica
+6.4,2.8,5.5,2.1,Iris-virginica
+6.8,3.1,5.7,2.2,Iris-virginica
+6.3,2.8,5.3,1.7,Iris-virginica
+7.4,2.8,6.0,1.9,Iris-virginica
+7.7,2.9,6.6,2.0,Iris-virginica
+6.5,3.0,5.9,2.2,Iris-virginica
+7.5,3.0,6.0,2.2,Iris-virginica
+6.0,2.7,5.0,1.9,Iris-virginica
+7.6,3.0,6.6,2.1,Iris-virginica
+6.4,2.8,5.7,2.1,Iris-virginica
+6.5,3.0,5.5,2.1,Iris-virginica
+6.9,3.0,5.7,2.1,Iris-virginica
+7.7,3.8,6.7,2.2,Iris-virginica
+6.5,3.0,5.5,1.8,Iris-virginica
+7.2,3.1,6.0,1.7,Iris-virginica
+6.3,2.5,5.2,1.7,Iris-virginica
+7.2,3.1,5.9,1.7,Iris-virginica
+6.2,2.9,4.8,1.8,Iris-virginica
+6.5,3.4,5.7,2.3,Iris-virginica
+6.8,3.1,5.3,2.3,Iris-virginica
+6.5,3.2,5.2,2.0,Iris-virginica
+6.6,2.5,5.7,1.7,Iris-virginica
+7.7,3.0,6.2,2.3,Iris-virginica
+6.1,2.9,4.8,1.8,Iris-virginica
+6.3,3.1,5.3,1.8,Iris-virginica
+7.7,2.9,6.7,2.0,Iris-virginica
+6.1,2.8,4.8,1.8,Iris-virginica
+5.8,2.7,5.2,1.9,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+6.8,3.1,5.7,2.4,Iris-virginica
+6.9,3.1,5.5,2.1,Iris-virginica
+6.4,2.7,5.3,1.9,Iris-virginica
+7.8,3.8,6.6,2.1,Iris-virginica
+6.0,3.0,5.0,1.8,Iris-virginica
+6.8,3.3,5.7,2.2,Iris-virginica
+6.3,2.6,5.0,1.9,Iris-virginica
+6.7,3.3,5.7,2.3,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+6.1,2.6,5.5,1.4,Iris-virginica
+6.1,2.3,5.0,1.6,Iris-virginica
+6.4,3.0,5.5,1.8,Iris-virginica
+6.4,2.8,5.5,2.0,Iris-virginica
+7.7,2.9,6.7,2.1,Iris-virginica
+6.3,2.8,4.8,1.8,Iris-virginica
+6.3,3.4,5.9,2.4,Iris-virginica
+6.6,3.0,5.5,2.3,Iris-virginica
+6.2,3.0,5.0,1.8,Iris-virginica
+6.9,3.1,5.2,2.2,Iris-virginica
+7.1,3.5,6.0,2.1,Iris-virginica
+6.7,3.3,5.7,2.5,Iris-virginica
+6.3,2.8,5.0,1.7,Iris-virginica
+5.3,2.6,4.8,1.8,Iris-virginica
+7.7,2.7,6.7,2.1,Iris-virginica
+6.8,3.1,5.5,2.1,Iris-virginica
+5.8,2.7,5.2,1.9,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+6.5,3.0,5.3,1.9,Iris-virginica
+7.7,2.8,6.7,2.1,Iris-virginica
+6.5,3.3,5.9,2.5,Iris-virginica
+7.1,3.4,5.9,2.0,Iris-virginica
+5.9,3.0,5.2,1.8,Iris-virginica
+6.6,3.0,5.5,2.0,Iris-virginica
+6.7,2.5,5.7,1.8,Iris-virginica
+6.3,3.0,5.5,1.8,Iris-virginica
+6.9,3.2,5.5,2.3,Iris-virginica
+6.5,2.5,5.3,1.9,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6.3,3.0,5.5,1.8,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6.8,3.1,5.7,2.2,Iris-virginica
+6.6,3.0,5.7,2.3,Iris-virginica
+6.3,3.1,5.3,1.8,Iris-virginica
+4.9,2.5,4.5,1.7,Iris-virginica
+6.3,2.5,5.2,1.9,Iris-virginica
+7.4,2.8,6.2,1.9,Iris-virginica
+6.3,3.1,5.3,1.8,Iris-virginica
+6.5,3.0,5.5,2.1,Iris-virginica
+6.3,3.4,5.7,2.4,Iris-virginica
+6.3,2.9,5.3,1.8,Iris-virginica
+5.9,3.0,5.0,1.8,Iris-virginica
+6.3,3.4,5.5,2.4,Iris-virginica
+6.4,2.7,5.3,1.9,Iris-virginica
+6.0,2.3,5.2,1.5,Iris-virginica
+6.3,2.5,5.0,1.9,Iris-virginica
+7.3,2.9,6.4,1.8,Iris-virginica
+6.1,3.0,5.0,1.8,Iris-virginica
+6.3,2.7,5.0,1.7,Iris-virginica
+5.8,3.0,5.0,1.8,Iris-virginica
+6.7,3.1,5.7,2.4,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+7.3,2.9,6.4,1.9,Iris-virginica
+7.2,3.0,6.0,1.7,Iris-virginica
+6.0,2.7,5.0,1.9,Iris-virginica
+6.0,3.0,4.8,1.8,Iris-virginica
+6.2,2.8,5.3,2.2,Iris-virginica
+5.9,3.0,5.2,1.8,Iris-virginica
+7.2,3.0,6.2,1.8,Iris-virginica
+6.9,3.1,5.5,2.1,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+7.7,2.6,6.9,2.3,Iris-virginica
+7.3,3.6,6.2,2.4,Iris-virginica
+7.2,3.2,6.0,1.8,Iris-virginica
+7.0,3.0,5.7,2.1,Iris-virginica
+6.8,3.1,5.7,2.4,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+6.3,2.8,5.0,1.7,Iris-virginica
+7.3,3.3,6.0,1.8,Iris-virginica
+7.6,3.0,6.6,2.0,Iris-virginica
+6.4,2.9,5.7,2.2,Iris-virginica
+6.3,2.8,5.5,2.0,Iris-virginica
+6.0,2.5,5.0,2.0,Iris-virginica
+6.3,3.3,5.9,2.5,Iris-virginica
+7.1,3.0,5.9,2.0,Iris-virginica
+7.7,3.0,6.4,2.1,Iris-virginica
+7.6,3.0,6.6,2.1,Iris-virginica
+6.0,3.0,4.8,1.8,Iris-virginica
+6.7,3.0,5.2,2.3,Iris-virginica
+6.6,3.3,5.5,2.0,Iris-virginica
+6.3,2.8,5.5,2.3,Iris-virginica
+7.6,2.8,6.4,2.0,Iris-virginica
+6.3,3.1,5.3,1.8,Iris-virginica
+7.0,3.0,5.7,2.1,Iris-virginica
+6.7,3.3,5.7,2.1,Iris-virginica
+6.3,3.1,5.3,1.8,Iris-virginica
+6.3,2.5,5.2,1.7,Iris-virginica
+6.1,3.0,4.8,1.8,Iris-virginica
+7.2,3.0,6.0,2.0,Iris-virginica
+7.5,3.0,6.6,2.0,Iris-virginica
+6.7,3.1,5.5,2.4,Iris-virginica
+6.6,3.3,5.5,2.0,Iris-virginica
+7.7,3.0,6.2,2.3,Iris-virginica
+6.0,3.0,4.8,1.8,Iris-virginica
+6.0,3.0,4.8,1.8,Iris-virginica
+6.9,3.1,5.2,2.3,Iris-virginica
+6.9,3.1,5.2,2.3,Iris-virginica
+6.5,3.0,5.3,1.9,Iris-virginica
+7.2,3.1,5.9,1.7,Iris-virginica
+6.4,3.4,5.7,2.3,Iris-virginica
+6.7,3.0,5.2,2.3,Iris-virginica
+6.3,2.8,5.2,1.5,Iris-virginica
+6.9,3.1,5.3,2.2,Iris-virginica
+7.5,2.8,6.6,1.9,Iris-virginica
+7.2,2.9,6.0,1.7,Iris-virginica
+6.3,2.9,5.5,1.8,Iris-virginica
+5.9,3.0,5.2,1.8,Iris-virginica
+7.1,3.0,5.9,2.1,Iris-virginica
+6.3,3.1,5.3,1.8,Iris-virginica
+6.3,3.0,5.5,1.8,Iris-virginica
+7.7,2.9,6.4,2.2,Iris-virginica
+6.7,2.5,5.9,1.8,Iris-virginica
+7.6,3.7,6.6,2.1,Iris-virginica
+6.4,2.8,5.5,2.1,Iris-virginica
+6.8,3.0,5.2,2.3,Iris-virginica
+6.8,3.1,5.9,2.3,Iris-virginica
+6.3,2.6,5.0,1.9,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+7.2,2.9,6.2,1.7,Iris-virginica
+6.7,3.3,5.7,2.2,Iris-virginica
+6.3,2.7,5.0,1.8,Iris-virginica
+7.7,2.9,6.7,2.0,Iris-virginica
+6.5,3.0,5.9,2.2,Iris-virginica
+6.5,3.0,5.3,1.9,Iris-virginica
+6.6,3.1,5.7,2.3,Iris-virginica
+6.1,2.9,5.0,1.8,Iris-virginica
+6.3,2.8,5.2,1.5,Iris-virginica
+6.5,3.0,5.7,2.2,Iris-virginica
+6.7,3.3,5.7,2.3,Iris-virginica
+6.4,2.8,5.5,2.1,Iris-virginica
+6.8,3.1,5.3,2.3,Iris-virginica
+6.0,2.8,5.0,1.9,Iris-virginica
+6.6,3.0,5.5,2.3,Iris-virginica
+6.4,2.7,5.3,1.9,Iris-virginica
+7.3,2.9,6.4,1.9,Iris-virginica
+5.8,2.7,5.0,1.9,Iris-virginica
+6.5,3.0,5.3,2.0,Iris-virginica
+7.2,3.0,6.2,1.8,Iris-virginica
+6.9,3.1,5.3,2.1,Iris-virginica
+5.9,3.0,5.2,1.8,Iris-virginica
+7.6,3.5,6.2,1.9,Iris-virginica
+7.7,2.7,6.9,2.3,Iris-virginica
+6.3,2.7,5.0,1.7,Iris-virginica
+7.3,2.9,5.9,1.7,Iris-virginica
+5.3,2.4,4.6,1.7,Iris-virginica
+6.0,2.2,5.0,1.5,Iris-virginica
+6.3,2.7,4.8,1.8,Iris-virginica
+7.3,2.9,6.2,1.8,Iris-virginica
+7.2,3.0,6.2,1.8,Iris-virginica
+7.5,2.8,6.6,1.9,Iris-virginica
+6.8,3.1,5.7,2.4,Iris-virginica
+6.4,2.8,5.7,2.2,Iris-virginica
+5.6,2.8,5.0,2.0,Iris-virginica
+6.5,3.0,5.5,1.8,Iris-virginica
+6.2,2.7,5.5,1.4,Iris-virginica
+6.7,3.0,5.3,2.3,Iris-virginica
+7.7,3.0,6.0,2.3,Iris-virginica
+5.8,2.7,5.2,1.9,Iris-virginica
+6.0,3.0,4.8,1.8,Iris-virginica
+7.7,2.9,6.6,2.0,Iris-virginica
+5.7,2.6,5.0,2.0,Iris-virginica
+6.4,3.1,5.3,1.9,Iris-virginica
+6.3,2.7,5.0,1.8,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+6.8,3.3,5.9,2.4,Iris-virginica
+7.3,2.9,6.4,1.8,Iris-virginica
+6.6,3.0,5.7,2.2,Iris-virginica
+7.2,3.0,6.2,1.8,Iris-virginica
+6.8,3.1,5.5,2.4,Iris-virginica
+6.8,3.1,5.3,2.3,Iris-virginica
+7.5,2.8,6.2,2.0,Iris-virginica
+6.1,3.0,5.0,1.8,Iris-virginica
+6.2,2.5,5.0,2.0,Iris-virginica
+7.2,3.1,5.9,1.7,Iris-virginica
+6.3,3.3,6.0,2.5,Iris-virginica
+6.4,3.1,5.3,1.9,Iris-virginica
+7.2,3.0,5.9,1.7,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+6.3,2.9,5.3,1.8,Iris-virginica
+5.0,2.4,4.6,1.7,Iris-virginica
+6.3,3.2,5.3,2.3,Iris-virginica
+6.3,2.8,5.5,2.0,Iris-virginica
+6.0,2.3,5.2,1.5,Iris-virginica
+7.6,2.8,6.6,2.0,Iris-virginica
+7.3,3.0,6.2,2.1,Iris-virginica
+7.2,3.1,5.9,1.7,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+7.0,3.1,5.9,2.2,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6.6,3.0,5.7,2.2,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+6.5,3.2,5.2,2.1,Iris-virginica
+6.4,3.1,5.3,1.9,Iris-virginica
+6.4,3.1,5.3,1.9,Iris-virginica
+7.5,2.8,6.2,2.0,Iris-virginica
+7.2,3.0,6.0,1.7,Iris-virginica
+6.6,2.6,5.7,1.9,Iris-virginica
+6.5,3.2,5.2,2.0,Iris-virginica
+6.5,3.0,5.5,1.8,Iris-virginica
+6.8,3.0,5.3,2.2,Iris-virginica
+7.4,3.0,6.4,2.0,Iris-virginica
+6.4,2.8,5.5,2.2,Iris-virginica
+6.7,3.2,5.9,2.3,Iris-virginica
+6.3,3.0,5.5,1.8,Iris-virginica
+6.3,2.7,4.8,1.8,Iris-virginica
+6.6,3.3,5.7,2.5,Iris-virginica
+7.3,3.6,6.0,2.0,Iris-virginica
+6.9,3.1,5.3,2.3,Iris-virginica
+7.6,3.0,6.6,2.1,Iris-virginica
+6.3,2.5,5.0,1.9,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+6.7,3.3,5.7,2.3,Iris-virginica
+6.5,2.4,5.5,1.7,Iris-virginica
+6.3,3.3,5.9,2.5,Iris-virginica
+7.2,3.0,6.2,1.8,Iris-virginica
+7.7,3.8,6.6,2.3,Iris-virginica
+5.8,2.6,5.0,2.1,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+6.5,3.0,5.5,1.8,Iris-virginica
+7.2,3.0,6.0,2.0,Iris-virginica
+7.2,3.2,6.0,1.8,Iris-virginica
+6.3,3.1,5.3,1.8,Iris-virginica
+6.3,2.7,5.2,1.9,Iris-virginica
+6.4,2.8,5.5,2.2,Iris-virginica
+6.4,2.7,5.3,1.9,Iris-virginica
+6.4,3.4,5.7,2.4,Iris-virginica
+5.9,3.0,5.0,1.8,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+5.7,2.5,5.0,2.0,Iris-virginica
+6.5,3.0,5.3,2.0,Iris-virginica
+6.9,3.1,5.2,2.3,Iris-virginica
+7.5,3.6,6.4,2.3,Iris-virginica
+6.3,2.8,5.2,1.5,Iris-virginica
+5.8,2.6,5.0,2.1,Iris-virginica
+7.3,2.9,6.2,1.8,Iris-virginica
+6.5,3.1,5.2,2.0,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+6.3,3.0,5.5,1.8,Iris-virginica
+6.4,2.8,5.5,2.0,Iris-virginica
+6.4,2.7,5.3,1.9,Iris-virginica
+7.7,2.8,6.6,2.3,Iris-virginica
+6.5,2.5,5.3,1.9,Iris-virginica
+7.1,3.0,5.9,2.1,Iris-virginica
+6.7,3.2,5.7,2.4,Iris-virginica
+6.3,3.3,5.9,2.5,Iris-virginica
+6.5,2.5,5.3,1.9,Iris-virginica
+6.7,3.1,5.5,2.4,Iris-virginica
+6.9,3.1,5.5,2.0,Iris-virginica
+6.5,3.0,5.7,2.2,Iris-virginica
+6.3,2.7,5.0,1.7,Iris-virginica
+7.1,3.4,5.9,2.4,Iris-virginica
+6.6,2.6,5.7,1.9,Iris-virginica
+6.8,3.3,5.7,2.4,Iris-virginica
+5.7,2.8,5.0,2.0,Iris-virginica
+7.2,3.1,5.9,1.7,Iris-virginica
+5.9,3.0,5.2,1.8,Iris-virginica
+5.7,2.5,5.0,2.0,Iris-virginica
+6.3,3.3,5.9,2.5,Iris-virginica
+7.7,3.0,6.0,2.3,Iris-virginica
+6.7,3.0,5.3,2.3,Iris-virginica
+7.8,3.8,6.6,2.1,Iris-virginica
+6.3,2.8,5.5,2.0,Iris-virginica
+7.5,2.8,6.6,1.9,Iris-virginica
+6.3,2.6,5.0,1.9,Iris-virginica
+6.3,2.8,5.2,1.5,Iris-virginica
+5.5,2.8,4.8,2.0,Iris-virginica
+6.4,3.2,5.2,2.2,Iris-virginica
+6.1,2.6,5.5,1.4,Iris-virginica
+7.3,2.9,6.4,1.9,Iris-virginica
+7.2,3.0,6.0,1.7,Iris-virginica
+5.7,2.8,5.0,2.2,Iris-virginica
+7.0,3.1,5.7,2.0,Iris-virginica
+7.2,3.6,6.0,2.5,Iris-virginica
+7.3,3.0,6.2,2.1,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+7.5,2.8,6.0,2.0,Iris-virginica
+6.7,3.0,5.3,2.3,Iris-virginica

data/sample.csv

@@ -0,0 +1,152 @@
+sepal length,sepal width,petal length,petal width,label
+5.1,3.5,,0.2,Iris-setosa
+4.9,,1.4,0.2,Iris-setosa
+4.7,3.2,1.3,0.2,Iris-setosa
+4.6,3.1,1.5,0.2,Iris-setosa
+5.0,3.6,1.4,0.2,Iris-setosa
+5.4,3.9,1.7,0.4,Iris-setosa
+4.6,3.4,1.4,0.3,Iris-setosa
+5.0,3.4,1.5,0.2,Iris-setosa
+4.4,2.9,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.4,3.7,1.5,0.2,Iris-setosa
+4.8,3.4,1.6,0.2,Iris-setosa
+4.8,3.0,1.4,0.1,Iris-setosa
+4.3,3.0,1.1,0.1,Iris-setosa
+5.8,4.0,1.2,0.2,Iris-setosa
+5.7,4.4,1.5,0.4,Iris-setosa
+5.4,3.9,1.3,0.4,Iris-setosa
+5.1,3.5,1.4,0.3,Iris-setosa
+5.7,3.8,1.7,0.3,Iris-setosa
+5.1,3.8,1.5,0.3,Iris-setosa
+5.4,3.4,1.7,0.2,Iris-setosa
+5.1,3.7,1.5,0.4,Iris-setosa
+4.6,3.6,1.0,0.2,Iris-setosa
+5.1,3.3,1.7,0.5,Iris-setosa
+4.8,3.4,1.9,0.2,Iris-setosa
+5.0,3.0,1.6,0.2,Iris-setosa
+5.0,3.4,1.6,0.4,Iris-setosa
+5.2,3.5,1.5,0.2,Iris-setosa
+5.2,3.4,1.4,0.2,Iris-setosa
+4.7,3.2,1.6,0.2,Iris-setosa
+4.8,3.1,1.6,0.2,Iris-setosa
+5.4,3.4,1.5,0.4,Iris-setosa
+5.2,4.1,1.5,0.1,Iris-setosa
+5.5,4.2,1.4,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+5.0,3.2,1.2,0.2,Iris-setosa
+5.5,3.5,1.3,0.2,Iris-setosa
+4.9,3.1,1.5,0.1,Iris-setosa
+4.4,3.0,1.3,0.2,Iris-setosa
+5.1,3.4,1.5,0.2,Iris-setosa
+5.0,3.5,1.3,0.3,Iris-setosa
+4.5,2.3,1.3,0.3,Iris-setosa
+4.4,3.2,1.3,0.2,Iris-setosa
+5.0,3.5,1.6,0.6,Iris-setosa
+5.1,3.8,1.9,0.4,Iris-setosa
+4.8,3.0,1.4,0.3,Iris-setosa
+5.1,3.8,1.6,0.2,Iris-setosa
+4.6,3.2,1.4,0.2,Iris-setosa
+5.3,3.7,1.5,0.2,Iris-setosa
+5.0,3.3,1.4,0.2,Iris-setosa
+7.0,3.2,4.7,1.4,Iris-versicolor
+6.4,3.2,4.5,1.5,Iris-versicolor
+6.9,3.1,4.9,1.5,Iris-versicolor
+5.5,2.3,4.0,1.3,Iris-versicolor
+6.5,2.8,4.6,1.5,Iris-versicolor
+5.7,2.8,4.5,1.3,Iris-versicolor
+6.3,3.3,4.7,1.6,Iris-versicolor
+4.9,2.4,3.3,1.0,Iris-versicolor
+6.6,2.9,4.6,1.3,Iris-versicolor
+5.2,2.7,3.9,1.4,Iris-versicolor
+5.0,2.0,3.5,1.0,Iris-versicolor
+5.9,3.0,4.2,1.5,Iris-versicolor
+6.0,2.2,4.0,1.0,Iris-versicolor
+6.1,2.9,4.7,1.4,Iris-versicolor
+5.6,2.9,3.6,1.3,Iris-versicolor
+6.7,3.1,4.4,1.4,Iris-versicolor
+5.6,3.0,4.5,1.5,Iris-versicolor
+5.8,2.7,4.1,1.0,Iris-versicolor
+6.2,2.2,4.5,1.5,Iris-versicolor
+5.6,2.5,3.9,1.1,Iris-versicolor
+5.9,3.2,4.8,1.8,Iris-versicolor
+6.1,2.8,4.0,1.3,Iris-versicolor
+6.3,2.5,4.9,1.5,Iris-versicolor
+6.1,2.8,4.7,1.2,Iris-versicolor
+6.4,2.9,4.3,1.3,Iris-versicolor
+6.6,3.0,4.4,1.4,Iris-versicolor
+6.8,2.8,4.8,1.4,Iris-versicolor
+6.7,3.0,5.0,1.7,Iris-versicolor
+6.0,2.9,4.5,1.5,Iris-versicolor
+5.7,2.6,3.5,1.0,Iris-versicolor
+5.5,2.4,3.8,1.1,Iris-versicolor
+5.5,2.4,3.7,1.0,Iris-versicolor
+5.8,2.7,3.9,1.2,Iris-versicolor
+6.0,2.7,5.1,1.6,Iris-versicolor
+5.4,3.0,4.5,1.5,Iris-versicolor
+6.0,3.4,4.5,1.6,Iris-versicolor
+6.7,3.1,4.7,1.5,Iris-versicolor
+6.3,2.3,4.4,1.3,Iris-versicolor
+5.6,3.0,4.1,1.3,Iris-versicolor
+5.5,2.5,4.0,1.3,Iris-versicolor
+5.5,2.6,4.4,1.2,Iris-versicolor
+6.1,3.0,4.6,1.4,Iris-versicolor
+5.8,2.6,4.0,1.2,Iris-versicolor
+5.0,2.3,3.3,1.0,Iris-versicolor
+5.6,2.7,4.2,1.3,Iris-versicolor
+5.7,3.0,4.2,1.2,Iris-versicolor
+5.7,2.9,4.2,1.3,Iris-versicolor
+6.2,2.9,4.3,1.3,Iris-versicolor
+5.1,2.5,3.0,1.1,Iris-versicolor
+5.7,2.8,4.1,1.3,Iris-versicolor
+6.3,3.3,6.0,2.5,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+7.1,3.0,5.9,2.1,Iris-virginica
+6.3,2.9,5.6,1.8,Iris-virginica
+6.5,3.0,5.8,2.2,Iris-virginica
+7.6,3.0,6.6,2.1,Iris-virginica
+4.9,2.5,4.5,1.7,Iris-virginica
+7.3,2.9,6.3,1.8,Iris-virginica
+6.7,2.5,5.8,1.8,Iris-virginica
+7.2,3.6,6.1,2.5,Iris-virginica
+6.5,3.2,5.1,2.0,Iris-virginica
+6.4,2.7,5.3,1.9,Iris-virginica
+6.8,3.0,5.5,2.1,Iris-virginica
+5.7,2.5,5.0,2.0,Iris-virginica
+5.8,2.8,5.1,2.4,Iris-virginica
+6.4,3.2,5.3,2.3,Iris-virginica
+6.5,3.0,5.5,1.8,Iris-virginica
+7.7,3.8,6.7,2.2,Iris-virginica
+7.7,2.6,6.9,2.3,Iris-virginica
+6.0,2.2,5.0,1.5,Iris-virginica
+6.9,3.2,5.7,2.3,Iris-virginica
+5.6,2.8,4.9,2.0,Iris-virginica
+7.7,2.8,6.7,2.0,Iris-virginica
+6.3,2.7,4.9,1.8,Iris-virginica
+6.7,3.3,5.7,2.1,Iris-virginica
+7.2,3.2,6.0,1.8,Iris-virginica
+6.2,2.8,4.8,1.8,Iris-virginica
+6.1,3.0,4.9,1.8,Iris-virginica
+6.4,2.8,5.6,2.1,Iris-virginica
+7.2,3.0,5.8,1.6,Iris-virginica
+7.4,2.8,6.1,1.9,Iris-virginica
+7.9,3.8,6.4,2.0,Iris-virginica
+6.4,2.8,5.6,2.2,Iris-virginica
+6.3,2.8,5.1,1.5,Iris-virginica
+6.1,2.6,5.6,1.4,Iris-virginica
+7.7,3.0,6.1,2.3,Iris-virginica
+6.3,3.4,5.6,2.4,Iris-virginica
+6.4,3.1,5.5,1.8,Iris-virginica
+6.0,3.0,4.8,1.8,Iris-virginica
+6.9,3.1,5.4,2.1,Iris-virginica
+6.7,3.1,5.6,2.4,Iris-virginica
+6.9,3.1,5.1,2.3,Iris-virginica
+5.8,2.7,5.1,1.9,Iris-virginica
+6.8,3.2,5.9,2.3,Iris-virginica
+6.7,3.3,5.7,2.5,Iris-virginica
+6.7,3.0,5.2,2.3,Iris-virginica
+6.3,2.5,5.0,1.9,Iris-virginica
+6.5,3.0,5.2,2.0,Iris-virginica
+6.2,3.4,5.4,2.3,Iris-virginica
+5.9,3.0,5.1,1.8,Iris-virginica
+

utils/file_utils.py

@@ -0,0 +1,14 @@
+import pandas as pd
+
+def load_csv(file_path):
+    try:
+        df = pd.read_csv(file_path)
+        return df, None
+    except Exception as e:
+        return None, str(e)
+
+def preview_dataframe(df, num_rows=5):
+    return df.head(num_rows)
+
+def get_column_names(df):
+    return list(df.columns)

utils/logger.py

@@ -0,0 +1,48 @@
+import os
+import json
+import time
+from datetime import datetime
+import joblib
+import psutil
+import platform
+
+try:
+    import cpuinfo
+    CPU_NAME = cpuinfo.get_cpu_info().get('brand_raw', platform.processor())
+except Exception:
+    CPU_NAME = platform.processor()
+
+def log_experiment_results(logs, log_dir="experiments/logs/", log_file="experiment_log.jsonl"):
+    os.makedirs(log_dir, exist_ok=True)
+    log_path = os.path.join(log_dir, log_file)
+    with open(log_path, "a") as f:
+        for entry in logs:
+            f.write(json.dumps(entry) + "\n")
+
+def create_log_entry(experiment_title, model_name, hyperparams, dataset_name, preprocessing, metrics, train_time, model_object):
+    timestamp = datetime.now().isoformat()
+    model_size = get_model_size(model_object)
+    cpu_util = psutil.cpu_percent(interval=0.1)
+    return {
+        "experiment_title": experiment_title,
+        "timestamp": timestamp,
+        "model": model_name,
+        "hyperparameters": hyperparams,
+        "dataset": dataset_name,
+        "preprocessing": preprocessing,
+        "metrics": metrics,
+        "training_time_sec": train_time,
+        "model_size_bytes": model_size,
+        "system_info": {
+            "cpu": CPU_NAME,
+            "cpu_utilization": cpu_util,
+            "memory_used_mb": psutil.Process().memory_info().rss // 1024 ** 2
+        }
+    }
+
+def get_model_size(model):
+    temp_path = "experiments/logs/_temp_model.joblib"
+    joblib.dump(model, temp_path)
+    size = os.path.getsize(temp_path)
+    os.remove(temp_path)
+    return size

utils/preprocessing.py

@@ -0,0 +1,45 @@
+
+import pandas as pd
+from sklearn.preprocessing import StandardScaler, LabelEncoder
+
+def preprocess_data(df, target_col, missing_strategy="drop", transformation_map=None):
+    df = df.copy()
+
+    # 1. Handle missing values
+    if missing_strategy == "drop":
+        df = df.dropna()
+    elif missing_strategy in ["mean", "median"]:
+        numeric_cols = df.select_dtypes(include=["number"]).columns
+        non_numeric_cols = df.columns.difference(numeric_cols)
+        if missing_strategy == "mean":
+            df[numeric_cols] = df[numeric_cols].fillna(df[numeric_cols].mean())
+        else:
+            df[numeric_cols] = df[numeric_cols].fillna(df[numeric_cols].median())
+        for col in non_numeric_cols:
+            if df[col].isna().sum() > 0:
+                df[col] = df[col].fillna(df[col].mode()[0])
+    elif missing_strategy == "mode":
+        for col in df.columns:
+            if df[col].isna().sum() > 0:
+                df[col] = df[col].fillna(df[col].mode()[0])
+
+    # 2. Apply feature transformations
+    if transformation_map:
+        for col, transform in transformation_map.items():
+            if transform == "Label Encode":
+                if df[col].dtype == "object" or str(df[col].dtype).startswith("category"):
+                    df[col] = LabelEncoder().fit_transform(df[col])
+                else:
+                    df[col] = LabelEncoder().fit_transform(df[col].astype(str))
+            elif transform == "Normalize":
+                scaler = StandardScaler()
+                df[[col]] = scaler.fit_transform(df[[col]])
+            # "No Transformation" = leave column as is
+
+    # 3. Label encode target column if it's a string
+    if target_col and target_col in df.columns:
+        if df[target_col].dtype == "object" or str(df[target_col].dtype).startswith("category"):
+            df[target_col] = LabelEncoder().fit_transform(df[target_col])
+
+    return df
+

utils/split.py

@@ -0,0 +1,6 @@
+from sklearn.model_selection import train_test_split
+
+def split_data(df, target_col, test_size=0.2, random_state=42):
+    X = df.drop(columns=[target_col])
+    y = df[target_col]
+    return train_test_split(X, y, test_size=test_size, random_state=random_state)

utils/training.py

@@ -0,0 +1,74 @@
+
+from sklearn.linear_model import LogisticRegression
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.svm import SVC
+from sklearn.naive_bayes import GaussianNB
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
+from time import perf_counter as timer  # ✅ Use perf_counter for better precision
+import joblib
+import os
+
+def get_model_instance(model_name, params):
+    if model_name == "Logistic Regression":
+        return LogisticRegression(**params)
+    elif model_name == "Decision Tree":
+        return DecisionTreeClassifier(**params)
+    elif model_name == "Random Forest":
+        return RandomForestClassifier(**params)
+    elif model_name == "SVM":
+        return SVC(**params)
+    elif model_name == "Naive Bayes":
+        return GaussianNB(**params)
+    else:
+        raise ValueError(f"Unsupported model: {model_name}")
+
+def train_models(X_train, X_test, y_train, y_test, selected_models, model_params, preprocessing_steps, experiment_name="DefaultExperiment", dataset_name="Uploaded CSV"):
+    results = {}
+    for model_name in selected_models:
+        params = model_params.get(model_name, {})
+        model = get_model_instance(model_name, params)
+
+        # ✅ Measure training time with high precision
+        start_time = timer()
+        model.fit(X_train, y_train)
+        training_time = round(timer() - start_time, 4)
+
+        # ✅ Measure model size
+        temp_model_path = f"models/{model_name.replace(' ', '_')}_temp.joblib"
+        os.makedirs("models", exist_ok=True)
+        joblib.dump(model, temp_model_path)
+        model_size = os.path.getsize(temp_model_path)
+        os.remove(temp_model_path)
+
+        # ✅ Predictions
+        y_train_pred = model.predict(X_train)
+        y_test_pred = model.predict(X_test)
+
+        # ✅ Inference time on a single sample
+        single_sample = X_test.iloc[[0]] if hasattr(X_test, "iloc") else X_test[0].reshape(1, -1)
+        start_inf = timer()
+        _ = model.predict(single_sample)
+        inference_time = round(timer() - start_inf, 6)
+
+        # ✅ Evaluation metrics
+        metrics = {
+            "accuracy_train": accuracy_score(y_train, y_train_pred),
+            "accuracy_test": accuracy_score(y_test, y_test_pred),
+            "precision_train": precision_score(y_train, y_train_pred, average='weighted', zero_division=0),
+            "precision_test": precision_score(y_test, y_test_pred, average='weighted', zero_division=0),
+            "recall_train": recall_score(y_train, y_train_pred, average='weighted', zero_division=0),
+            "recall_test": recall_score(y_test, y_test_pred, average='weighted', zero_division=0),
+            "f1_score_train": f1_score(y_train, y_train_pred, average='weighted', zero_division=0),
+            "f1_score_test": f1_score(y_test, y_test_pred, average='weighted', zero_division=0),
+            "inference_time": inference_time
+        }
+
+        results[model_name] = {
+            "model": model,
+            "metrics": metrics,
+            "training_time": training_time,
+            "inference_time": inference_time,
+            "model_size": model_size
+        }
+    return results