同时预测,Fail

Author: makelove

ch46-机器学习-K近邻/2-英文字母的OCR.py

@@ -28,14 +28,11 @@
 
 correct = np.count_nonzero(result == labels)
 accuracy = correct * 100.0 / 10000
-print(accuracy)
+print('准确率', accuracy)#93.06
 #准确率 到了 93.22%。同样你可以  增加训练样本的数量来提 准确率。
 
-with np.load('knn_data_alphabet.npz') as data:
-    print(data.files)
-    train = data['train']
-    train_labels = data['train_labels']
-    test = data['test']
-    test_labels = data['test_labels']
+
+# save the data
+np.savez('knn_data_alphabet.npz', train_alphabet=train, train_labels_alphabet=responses,test_alphabet=testData,test_labels_alphabet=labels)
 
 #怎样预测字母？跟预测数字的一样

ch46-机器学习-K近邻/knn_data_alphabet.npz

@@ -8,3 +8,40 @@
 同时预测数字和英文字母1.py:
 """
 
+
+
+import numpy as np
+import cv2
+from matplotlib import pyplot as plt
+
+with np.load('knn_data_num.npz') as data:
+    print(data.files)  # ['train', 'train_labels', 'test', 'test_labels']
+    train = data['train']
+    train_labels = data['train_labels']
+    test = data['test']
+    test_labels = data['test_labels']
+
+with np.load('knn_data_alphabet.npz') as data:
+    print(data.files)
+    train_alphabet = data['train_alphabet']
+    train_labels_alphabet = data['train_labels_alphabet']
+    test_alphabet = data['test_alphabet']
+    test_labels_alphabet = data['test_labels_alphabet']
+
+# shape不一致，无法合并
+# train.shape #(2500, 400)
+# train_alphabet.shape#(10000, 17)
+# print('合并-数字-字母数据')
+# train = np.append(train, train_alphabet)
+# tratrain_labelsin = np.append(train_labels,train_labels_alphabet)
+# test = np.append(test, test_alphabet)
+# test_labels = np.append(test_labels, test_labels_alphabet)
+
+print('加载KNN,数据')
+knn = cv2.ml.KNearest_create()
+knn.train(train, cv2.ml.ROW_SAMPLE, train_labels)
+knn.train(train_alphabet, cv2.ml.ROW_SAMPLE, train_labels_alphabet)
+
+ret, result, neighbours, dist = knn.findNearest(
+    test, k=5)# shape不一致
+#出错，knearest.cpp:325: error: (-215) test_samples.type() == CV_32F && test_samples.cols == samples.cols in function findNearest

ch46-机器学习-K近邻/knn_data.npz renamed to ch46-机器学习-K近邻/knn_data_num.npz

@@ -6,14 +6,17 @@
 
 """
 预测手写数字1.py:
+
+验证码
+https://login.bthhotels.com/
 """
 
 import numpy as np
 import cv2
 from matplotlib import pyplot as plt
 
 with np.load('knn_data_num.npz') as data:
-    print(data.files)
+    print(data.files)  # ['train', 'train_labels', 'test', 'test_labels']
     train = data['train']
     train_labels = data['train_labels']
     test = data['test']
@@ -22,7 +25,6 @@
 print('加载KNN,数据')
 knn = cv2.ml.KNearest_create()
 knn.train(train, cv2.ml.ROW_SAMPLE, train_labels)
-ret, result, neighbours, dist = knn.findNearest(test, k=5)
 
 # 加载相片
 print('加载相片')