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--- |
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datasets: |
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- scikit-learn/iris |
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--- |
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```python |
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import joblib |
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model = joblib.load("iris_svm.joblib") |
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import json |
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with open("config.json", "r") as f: |
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config = json.load(f) |
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features = config["features"] |
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target = config["targets"][0] |
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target_mapping = config["target_mapping"] |
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import numpy as np |
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# example input data |
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input_data = np.array([ |
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[5.1, 3.5, 1.4, 0.2], |
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[4.9, 3.0, 1.4, 0.2], |
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[6.2, 3.4, 5.4, 2.3] |
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]) |
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# make sure the input data has the correct shape |
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if input_data.shape[1] != len(features): |
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raise ValueError(f"Input data must have {len(features)} features.") |
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predicted_classes = model.predict(input_data) |
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predicted_class_names = [list(target_mapping.keys())[list(target_mapping.values()).index(predicted_class)] for predicted_class in predicted_classes] |
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print("Predicted classes:", predicted_class_names) |
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``` |