perceptron

binary_svm.py

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+import pandas as pd
+import numpy as np
+import csv as csv
+import cv2
+
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.decomposition import RandomizedPCA
+from sklearn.cross_validation import train_test_split
+from sklearn.metrics import accuracy_score
+from sklearn import svm
+
+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))
+    print features.shape
+    return features
+
+train_raw=pd.read_csv('data/train_binary.csv',header=0)
+train = train_raw.values
+
+
+print 'Start PCA to 50'
+train_x=train[0::,1::]
+train_label=train[::,0]
+
+features = get_hog_features(train_x)
+
+# # pca
+# pca = RandomizedPCA(n_components=50, whiten=True).fit(train_x)
+# train_x_pca = pca.transform(train_x)
+# test_x_pca = pca.transform(test)
+# print train_x
+a_train, b_train, a_label, b_label = train_test_split(features, train_label, test_size=0.33, random_state=23323)
+print a_train.shape
+print a_label.shape
+
+print 'Start training'
+rbf_svc = svm.SVC(kernel='rbf')
+rbf_svc.fit(a_train,a_label)
+print 'Start predicting'
+b_predict=rbf_svc.predict(b_train)
+score=accuracy_score(b_label,b_predict)
+print "The accruacy socre is ", score