add model2

Author: caiiiac

MachineLearning/期末作业/use_KNN_model_2.py

@@ -0,0 +1,51 @@
+import numpy as np
+from sklearn import neighbors
+
+# fileName: 文件名字
+# isTest: =True为测试数据 label直接设0
+def readDataSet(fileName, isTest):
+	fr = open(fileName,encoding='utf-8')
+	lines = fr.readlines()
+
+	numLabels = len(lines) 
+	labels = np.zeros(numLabels)
+	dateSet = np.zeros([numLabels,54],int)
+
+	# 逐行读取数据到dataSet,labels
+	# 总共55列,前54列是样本特征,最后一列是样本类别(label)
+	for i in range(numLabels):
+		line = lines[i]
+		label = 0
+		if isTest:
+			label = 0
+		else:
+			label = line.split(' ')[54]
+		labels[i] = label
+
+		dates = np.zeros(54)
+		for j in range(53):
+			dates[j] = line.split(' ')[j]
+		dateSet[i] = dates
+
+	fr.close()
+	return dateSet,labels
+
+
+# read dataSet
+train_dataSet,train_labels = readDataSet('data_train.txt', False)
+
+
+knn = neighbors.KNeighborsClassifier(algorithm='kd_tree', n_neighbors=3)
+print(knn)
+knn.fit(train_dataSet,train_labels)
+
+# read test_dataSet
+test_dataSet,test_labels = readDataSet('data_test.txt', True)
+res = knn.predict(test_dataSet)
+
+# 输出预测结果,保存到model_1.txt
+mf = open('model_2.txt','w')
+
+for i in range(len(res)):
+	mf.write(str(res[i]) + '\n')
+mf.close()