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import pandas as pd
import numpy as np
import time
# from sklearn.cross_validation import train_test_split
# from sklearn.metrics import accuracy_score
def Predict(testset, trainset, train_labels):
predict = [] # 保存测试集预测到的label，并返回
count = 0 # 当前测试数据为第count个
for test_vec in testset:
# 输出当前运行的测试用例坐标，用于测试
count += 1
print("the number of %d is predicting..." % count)
knn_list = [] # 当前k个最近邻居
max_index = -1 # 当前k个最近邻居中距离最远点的坐标
max_dist = 0 # 当前k个最近邻居中距离最远点的距离
# 初始化knn_list,将前k个点的距离放入knn_list中
for i in range(k):
label = train_labels[i]
train_vec = trainset[i]
dist = np.linalg.norm(train_vec - test_vec) # 计算两个点的欧氏距离
knn_list.append((dist, label))
# 剩下的点
for i in range(k, len(train_labels)):
label = train_labels[i]
train_vec = trainset[i]
dist = np.linalg.norm(train_vec - test_vec) # 计算两个点的欧氏距离
# 寻找k个邻近点中距离最远的点
if max_index < 0:
for j in range(k):
if max_dist < knn_list[j][0]:
max_index = j
max_dist = knn_list[max_index][0]
# 如果当前k个最近邻中存在点距离比当前点距离远，则替换
if dist < max_dist:
knn_list[max_index] = (dist, label)
max_index = -1
max_dist = 0
# 统计选票
class_total = k
class_count = [0 for i in range(class_total)]
for dist, label in knn_list:
class_count[label] += 1
# 找出最大选票
mmax = max(class_count)
# 找出最大选票标签
for i in range(class_total):
if mmax == class_count[i]:
predict.append(i)
break
return np.array(predict)
k = 10 # 选取k值
if __name__ == '__main__':
print("Start read data")
time_1 = time.time()
raw_data = pd.read_csv('../data/train.csv', header=0) # 读取csv数据
data = raw_data.values
features = data[::, 1::]
labels = data[::, 0]
# 避免过拟合，采用交叉验证，随机选取33%数据作为测试集，剩余为训练集
train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size=0.33,
random_state=0)
time_2 = time.time()
print('read data cost %f seconds' % (time_2 - time_1))
print('Start training')
print('knn need not train')
time_3 = time.time()
print('training cost %f seconds' % (time_3 - time_2))
print('Start predicting')
test_predict = Predict(test_features, train_features, train_labels)
time_4 = time.time()
print('predicting cost %f seconds' % (time_4 - time_3))
score = accuracy_score(test_labels, test_predict)
print("The accruacy score is %f" % score)
# encoding=utf-8
import pandas as pd
import numpy as np
import time
# from sklearn.neighbors import KNeighborsClassifier
#
# from sklearn.cross_validation import train_test_split
if __name__ == '__main__':
print("Start read data...")
time_1 = time.time()
raw_data = pd.read_csv('../data/train.csv', header=0) # 读取csv数据
data = raw_data.values
features = data[::, 1::]
labels = data[::, 0]
# 随机选取33%数据作为测试集，剩余为训练集
train_features, test_features, train_labels, test_labels = train_test_split(features, labels, test_size=0.33,
random_state=0)
time_2 = time.time()
print('read data cost %f seconds' % (time_2 - time_1))
print('Start training...')
neigh = KNeighborsClassifier(n_neighbors=10)
neigh.fit(train_features, train_labels)
time_3 = time.time()
print('training cost %f seconds...' % (time_3 - time_2))
print('Start predicting...')
test_predict = neigh.predict(test_features)
time_4 = time.time()
print('predicting cost %f seconds' % (time_4 - time_3))
score = neigh.score(test_features, test_labels)
print("The accruacy score is %f" % score)
