knn slow version

Author: WenDesi

knn/distance_test.py

@@ -0,0 +1,24 @@
+#encoding=utf-8
+
+import pandas as pd
+import numpy as np
+import time
+
+if __name__ == '__main__':
+    vec_1 = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
+    vec_2 = [2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,]
+
+    vec_1 = np.array(vec_1)
+    vec_2 = np.array(vec_2)
+
+    time_1 = time.time()
+
+    print np.sqrt(np.sum(np.square(vec_1 - vec_2)))
+
+    time_2 = time.time()
+    print time_2-time_1
+
+    print np.linalg.norm(vec_1 - vec_2)
+
+    time_3 = time.time()
+    print time_3-time_2

knn/knn.py

@@ -0,0 +1,111 @@
+#encoding=utf-8
+
+import pandas as pd
+import numpy as np
+import cv2
+import random
+import time
+
+from sklearn.cross_validation import train_test_split
+from sklearn.metrics import accuracy_score
+
+
+# 利用opencv获取图像hog特征
+def get_hog_features(trainset):
+    features = []
+
+    hog = cv2.HOGDescriptor('../hog.xml')
+
+    for img in trainset:
+        img = np.reshape(img,(28,28))
+        cv_img = img.astype(np.uint8)
+
+        hog_feature = hog.compute(cv_img)
+        # hog_feature = np.transpose(hog_feature)
+        features.append(hog_feature)
+
+    features = np.array(features)
+    features = np.reshape(features,(-1,324))
+
+    return features
+
+def Predict(testset,trainset,train_labels):
+    predict = []
+    count = 0
+    for test_vec in testset:
+        print count
+        count += 1
+
+        knn_list = []
+
+        for i in range(len(train_labels)):
+            label = train_labels[i]
+            train_vec = trainset[i]
+
+            dist = np.linalg.norm(train_vec - test_vec)
+
+            if len(knn_list) < k:                               # 如果还不够10个邻近点则直接添加即可
+                knn_list.append((dist,label))
+            else:
+                max_index = -1
+                max_dist = dist
+
+                # 寻找10个邻近点钟距离最远的点
+                for j in range(k):
+                    if max_dist < knn_list[j][0]:
+                        max_index = j
+                        max_dist = knn_list[max_index][0]
+
+                if max_index >= 0:
+                    knn_list[max_index] = (dist,label)
+
+        class_total = 10
+        class_count = [0 for i in range(class_total)]
+        for dist,label in knn_list:
+            class_count[label] += 1
+
+        mmax = max(class_count)
+
+        for i in range(class_total):
+            if mmax == class_count[i]:
+                predict.append(i)
+                break
+
+    return np.array(predict)
+
+k = 10
+
+if __name__ == '__main__':
+
+    print 'Start read data'
+
+    time_1 = time.time()
+
+    raw_data = pd.read_csv('../data/train.csv',header=0)
+    data = raw_data.values
+
+    imgs = data[0::,1::]
+    labels = data[::,0]
+
+    features = get_hog_features(imgs)
+
+    # 选取 2/3 数据作为训练集， 1/3 数据作为测试集
+    train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size=0.33, random_state=23323)
+    # print train_features.shape
+    # print train_features.shape
+
+    time_2 = time.time()
+    print 'read data cost ',time_2 - time_1,' second','\n'
+
+    print 'Start training'
+    print 'knn do not need to train'
+    time_3 = time.time()
+    print 'training cost ',time_3 - time_2,' second','\n'
+
+    print 'Start predicting'
+    test_predict = Predict(test_features,train_features,train_labels)
+    time_4 = time.time()
+    print 'predicting cost ',time_4 - time_3,' second','\n'
+
+    score = accuracy_score(test_labels,test_predict)
+    print "The accruacy socre is ", score