From 29a0f7e49a95c3cb4f0207afb3d46e69ace9d47b Mon Sep 17 00:00:00 2001
From: Qintefbr8 <1104320156@qq.com>
Date: Sat, 6 Nov 2021 18:44:15 +0800
Subject: [PATCH] ADD file via upload

---
 knn.py | 37 +++++++++++++++++++++++++++++++++++++
 1 file changed, 37 insertions(+)
 create mode 100644 knn.py

diff --git a/knn.py b/knn.py
new file mode 100644
index 0000000..b3a0a6e
--- /dev/null
+++ b/knn.py
@@ -0,0 +1,37 @@
+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_))
+