49 lines
1.5 KiB
Python
49 lines
1.5 KiB
Python
from sklearn.feature_extraction import DictVectorizer
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from sklearn.linear_model import LogisticRegression
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from sklearn.pipeline import Pipeline
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class FeatureExtractor:
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def fit(self, X, y=None):
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return self
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def transform(self, X):
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return [self.extract_features(pixels) for pixels in X]
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def extract_features(self, pixels):
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"""Extract hand-designed features from a 784-pixel image.
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Add at least two features of your own. Each feature should be a
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number computed from the pixel array.
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Arguments:
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pixels: numpy array of 784 float values in [0, 1]
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Returns:
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dict: feature name -> numerical value
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"""
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img = pixels.reshape(28, 28)
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return {
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"mean_brightness": float(pixels.mean()),
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"top_half_brightness": float(img[:14, :].mean()),
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"bottom_half_brightness": float(img[14:, :].mean()),
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# ---- Add your features here ----
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# "left_half_brightness": float(img[:, :14].mean()),
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# "right_half_brightness": float(img[:, 14:].mean()),
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# "num_bright_pixels": float((pixels > 0.5).sum()),
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}
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class HandPickedClassifier:
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def fit(self, X, y):
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self._pipeline = Pipeline([
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("features", FeatureExtractor()),
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("vectorizer", DictVectorizer()),
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("classifier", LogisticRegression(max_iter=1000)),
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])
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self._pipeline.fit(X, y)
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return self
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def predict(self, X):
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return self._pipeline.predict(X)
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