knn.predict(test[3:5])

Author: makelove

ch46-机器学习-K近邻/2-使用kNN对手写数字OCR.py

@@ -41,7 +41,7 @@
 matches = result == test_labels
 correct = np.count_nonzero(matches)
 accuracy = correct * 100.0 / result.size
-print('准确率', accuracy)  # 准确率91%
+print('准确率', accuracy)  # 准确率91.76%
 
 ''''''
 # save the data
@@ -56,5 +56,5 @@
 
 
 #TODO 怎样预测数字？
-# knn.predict?
+retval, results=knn.predict(test[3:5])
 # Docstring: predict(samples[, results[, flags]]) -> retval, results

ch46-机器学习-K近邻/knn-find_nearest.py

@@ -0,0 +1,49 @@
+# -*- coding: utf-8 -*-
+# @Time    : 2017/8/8 12:33
+# @Author  : play4fun
+# @File    : knn-find_nearest.py
+# @Software: PyCharm
+
+"""
+knn-find_nearest.py:
+http://www.bogotobogo.com/python/OpenCV_Python/python_opencv3_Machine_Learning_Classification_K-nearest_neighbors_k-NN.php
+"""
+
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Feature set containing (x,y) values of 25 known/training data
+trainData = np.random.randint(0, 100, (25, 2)).astype(np.float32)
+
+# Labels each one either Red or Blue with numbers 0 and 1
+responses = np.random.randint(0, 2, (25, 1)).astype(np.float32)
+
+# plot Reds
+red = trainData[responses.ravel() == 0]
+plt.scatter(red[:, 0], red[:, 1], 80, 'r', '^')
+
+# plot Blues
+blue = trainData[responses.ravel() == 1]
+plt.scatter(blue[:, 0], blue[:, 1], 80, 'b', 's')
+
+# CvKNearest instance
+# knn = cv2.KNearest()
+knn = cv2.ml.KNearest_create()
+# trains the model
+knn.train(trainData, responses)#TODO
+#TypeError: only length-1 arrays can be converted to Python scalars
+
+
+# New sample : (x,y)
+newcomer = np.random.randint(0, 100, (1, 2)).astype(np.float32)
+plt.scatter(newcomer[:, 0], newcomer[:, 1], 80, 'g', 'o')
+
+# Finds the 3nearest  neighbors and predicts responses for input vectors
+ret, results, neighbours, dist = knn.find_nearest(newcomer, 3)
+
+print("result: ", results, "\n")
+print("neighbours: ", neighbours, "\n")
+print("distance: ", dist)
+
+plt.show()