savez knn_data.npz

ch46-机器学习-K近邻/2-使用kNN对手写数字OCR.py

@@ -43,14 +43,18 @@
 accuracy = correct * 100.0 / result.size
 print('准确率', accuracy)  # 准确率91%
 
-#
+''''''
 # save the data
-np.savez('knn_data.npz', train=train, train_labels=train_labels)
+np.savez('knn_data.npz', train=train, train_labels=train_labels,test=test,test_labels=test_labels)
 # Now load the data
 with np.load('knn_data.npz') as data:
     print(data.files)
     train = data['train']
     train_labels = data['train_labels']
+    test = data['test']
+    test_labels = data['test_labels']
 
 
 #TODO 怎样预测数字？
+# knn.predict?
+# Docstring: predict(samples[, results[, flags]]) -> retval, results

ch46-机器学习-K近邻/预测手写数字1.py

@@ -0,0 +1,24 @@
+# -*- coding: utf-8 -*-
+# @Time    : 2017/8/8 11:57
+# @Author  : play4fun
+# @File    : 预测手写数字1.py
+# @Software: PyCharm
+
+"""
+预测手写数字1.py:
+"""
+
+import numpy as np
+import cv2
+from matplotlib import pyplot as plt
+
+with np.load('knn_data.npz') as data:
+    print(data.files)
+    train = data['train']
+    train_labels = data['train_labels']
+    test = data['test']
+    test_labels = data['test_labels']
+
+knn = cv2.ml.KNearest_create()
+knn.train(train, cv2.ml.ROW_SAMPLE, train_labels)
+ret, result, neighbours, dist = knn.findNearest(test, k=5)