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knn.py

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+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.preprocessing import LabelEncoder
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import classification_report
+from simplepreprocessor import SimplePreprocessor
+from simpledatasetloader import SimpleDatasetLoader
+from imutils import paths
+import argparse
+
+if __name__ == '__main__':
+    ap = argparse.ArgumentParser()
+    ap.add_argument("-d", "--dataset", required=True, help="path to input dataset")
+    ap.add_argument("-k", "--neighbors", type=int, default=1, help="of nearest neighbors for classification")
+    ap.add_argument("-j", "--jobs", type=int, help="of jobs for K-NN distance (-1 uses all variables cores)")
+    args = vars(ap.parse_args())
+
+    print("[INFO] loading images...")
+    imagePaths = list(paths.list_images(args["dataset"]))
+
+    sp = SimplePreprocessor(32, 32)
+    sdl = SimpleDatasetLoader(preprocessors=[sp])
+    (data, labels) = sdl.load(imagePaths, verbose=100)
+    data = data.reshape((data.shape[0], 3072))
+
+    print("[INFO] features matrix:{:.1f}MB".format(data.nbytes / (1024 *1000.0)))
+
+    le = LabelEncoder()
+    labels = le.fit_transform(labels)
+
+    (trainX, testX, trainY, testY) = train_test_split(data, labels, test_size=0.25, random_state=42)
+
+    print("[INFO] evaluating K-NN classifier...")
+
+    model =KNeighborsClassifier(n_neighbors=3)
+    model.fit(trainX, trainY)
+    print(classification_report(testY, model.predict(testX), target_names=le.classes_))
+