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+6.55381795953901 1.47332188122579 +8.41938640019952 7.29075946387086 +7.57223913040838 2.26004190249210 +6.25662399950607 0.566501191933395 +9.15677335584760 7.17513606222610 +8.35984503433578 1.91891766916066 +6.34920625597898 0.120424501924355 +4.82733388192721 1.19687959104710 +2.45336269880575 0.259812107633635 diff --git a/Chapter_10 KMeans/KMeans.py b/Chapter_10 KMeans/KMeans.py index d9d01f4..d0ab004 100644 --- a/Chapter_10 KMeans/KMeans.py +++ b/Chapter_10 KMeans/KMeans.py @@ -60,7 +60,7 @@ def kmeans(data, k, centroids): while change == True: change = False # 重置 for i in xrange(m): - minDist = np.inf # 设置样本与聚类中心之间的最小的距离，初始值为争取穷 + minDist = np.inf # 设置样本与聚类中心之间的最小的距离，初始值为正无穷 minIndex = 0 # 所属的类别 for j in xrange(k): # 计算i和每个聚类中心之间的距离 diff --git a/Chapter_2 Softmax Regression/softmax_regression_train.py b/Chapter_2 Softmax Regression/softmax_regression_train.py index fe4ccea..583d7bf 100644 --- a/Chapter_2 Softmax Regression/softmax_regression_train.py +++ b/Chapter_2 Softmax Regression/softmax_regression_train.py @@ -89,7 +89,7 @@ def save_model(file_name, weights): feature, label, k = load_data(inputfile) # 2、训练Softmax模型 print "---------- 2.training ------------" - weights = gradientAscent(feature, label, k, 5000, 0.2) + weights = gradientAscent(feature, label, k, 10000, 0.4) # 3、保存最终的模型 print "---------- 3.save model ------------" save_model("weights", weights) diff --git a/Chapter_3 Factorization Machine/FM_test.py b/Chapter_3 Factorization Machine/FM_test.py index 269e3c7..0b2c72c 100644 --- a/Chapter_3 Factorization Machine/FM_test.py +++ b/Chapter_3 Factorization Machine/FM_test.py @@ -52,15 +52,13 @@ def save_result(file_name, result): input: file_name(string)需要保存的文件名 result(mat):对测试数据的预测结果 ''' - f_result = open(file_name, "w") - m = np.shape(result)[0] - for i in xrange(m): - f_result.write(str(result[i, 0]) + "\n") - f_result.close() + f = open(file_name, "w") + f.write("\n".join(str(x) for x in result)) + f.close() if __name__ == "__main__": # 1、导入测试数据 - dataTest = loadDataSet("data_test.txt") + dataTest = loadDataSet("test_data.txt") # 2、导入FM模型 w0, w , v = loadModel("weights") # 3、预测 diff --git a/Chapter_3 Factorization Machine/FM_train.py b/Chapter_3 Factorization Machine/FM_train.py index 37d9435..204ce8a 100644 --- a/Chapter_3 Factorization Machine/FM_train.py +++ b/Chapter_3 Factorization Machine/FM_train.py @@ -23,8 +23,7 @@ def loadDataSet(data): lineArr.append(float(lines[i])) dataMat.append(lineArr) - #labelMat.append(float(lines[-1]) * 2 - 1) # 转换成{-1,1} - labelMat.append(float(lines[-1])) + labelMat.append(float(lines[-1]) * 2 - 1) # 转换成{-1,1} fr.close() return dataMat, labelMat @@ -34,7 +33,7 @@ def sigmoid(inx): def initialize_v(n, k): '''初始化交叉项 input: n(int)特征的个数 - k(int)FM模型的交叉向量维度 + k(int)FM模型的超参数 output: v(mat):交叉项的系数权重 ''' v = np.mat(np.zeros((n, k))) @@ -84,7 +83,7 @@ def stocGradAscent(dataMatrix, classLabels, k, max_iter, alpha): # 计算损失函数的值 if it % 1000 == 0: print "\t------- iter: ", it, " , cost: ", \ - getCost(getPrediction(np.mat(dataTrain), w0, w, v), classLabels) + getCost(getPrediction(np.mat(dataMatrix), w0, w, v), classLabels) # 3、返回最终的FM模型的参数 return w0, w, v @@ -174,7 +173,7 @@ def save_model(file_name, w0, w, v): dataTrain, labelTrain = loadDataSet("data_1.txt") print "---------- 2.learning ---------" # 2、利用随机梯度训练FM模型 - w0, w, v = stocGradAscent(np.mat(dataTrain), labelTrain, 2, 20000, 0.01) + w0, w, v = stocGradAscent(np.mat(dataTrain), labelTrain, 3, 10000, 0.01) predict_result = getPrediction(np.mat(dataTrain), w0, w, v) # 得到训练的准确性 print "----------training accuracy: %f" % (1 - getAccuracy(predict_result, labelTrain)) print "---------- 3.save result ---------" diff --git a/Chapter_3 Factorization Machine/test_data.txt b/Chapter_3 Factorization Machine/test_data.txt new file mode 100644 index 0000000..f8650e9 --- /dev/null +++ b/Chapter_3 Factorization Machine/test_data.txt @@ -0,0 +1,200 @@ +4.22885689100085 -0.383600664906212 +5.98523668756741 -0.401814062733461 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+7.81377051799277 -0.327592448772205 +6.92531986386519 -0.274004053493805 +3.96520792581593 -0.347683790060566 +7.80175531491174 -0.317975871659649 +6.07865907262946 -0.237887651602709 +1.04813241973500 -0.274359906785175 +5.49540107015198 -0.283385560614568 +8.90475679184438 -0.229599130878489 +7.34341083695970 -0.371524950619845 +0.728852990989761 -0.260383341617659 +7.98350864113952 -0.201049508579053 diff --git a/Chapter_5 Random Forest/random_forests_train.py b/Chapter_5 Random Forest/random_forests_train.py index 58796ab..1b987fb 100644 --- a/Chapter_5 Random Forest/random_forests_train.py +++ b/Chapter_5 Random Forest/random_forests_train.py @@ -141,10 +141,10 @@ def save_model(trees_result, trees_feature, result_file, feature_file): if __name__ == "__main__": # 1、导入数据 print "----------- 1、load data -----------" - data_train = load_data("data_2.txt") + data_train = load_data("data.txt") # 2、训练random_forest模型 print "----------- 2、random forest training ------------" - trees_result, trees_feature = random_forest_training(data_train, 1) + trees_result, trees_feature = random_forest_training(data_train, 50) # 3、得到训练的准确性 print "------------ 3、get prediction correct rate ------------" result = get_predict(trees_result, trees_feature, data_train) diff --git a/Chapter_6 BP/bp_test.py b/Chapter_6 BP/bp_test.py index 9f43bb1..8544ce4 100644 --- a/Chapter_6 BP/bp_test.py +++ b/Chapter_6 BP/bp_test.py @@ -34,11 +34,11 @@ def generate_data(): data[i, 1] = x[i, 1] * 9 - 4.5 # 2、将数据点保存到文件“test_data”中 f = open("test_data", "w") - m,n = np.shape(dataTest) + m,n = np.shape(data) for i in xrange(m): tmp =[] for j in xrange(n): - tmp.append(str(dataTest[i,j])) + tmp.append(str(data[i,j])) f.write("\t".join(tmp) + "\n") f.close() diff --git a/Chapter_7 LinearRegression/data_test.txt b/Chapter_7 LinearRegression/data_test.txt new file mode 100644 index 0000000..4c63a19 --- /dev/null +++ b/Chapter_7 LinearRegression/data_test.txt @@ -0,0 +1,200 @@ +0.58661363181 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a/Chapter_8 RidgeRegression/ridge_regression_test.py +++ b/Chapter_8 RidgeRegression/ridge_regression_test.py @@ -55,7 +55,7 @@ def save_result(file_name, predict): if __name__ == "__main__": # 1、导入测试数据 print "----------1.load data ------------" - testData = load_data("data.txt") + testData = load_data("data_test.txt") # 2、导入线性回归模型 print "----------2.load model ------------" w = load_model("weights") diff --git a/Chapter_8 RidgeRegression/ridge_regression_train.py b/Chapter_8 RidgeRegression/ridge_regression_train.py index 3483718..f9a7810 100644 --- a/Chapter_8 RidgeRegression/ridge_regression_train.py +++ b/Chapter_8 RidgeRegression/ridge_regression_train.py @@ -208,7 +208,7 @@ def save_weights(file_name, w0): if method == "bfgs": # 选择BFGS训练模型 w0 = bfgs(feature, label, 0.5, 1000) elif method == "lbfgs": # 选择L-BFGS训练模型 - w0 = lbfgs(feature, label, 0.5, 10, m=10) + w0 = lbfgs(feature, label, 0.5, 1000, m=10) else: # 使用最小二乘的方法 w0 = ridge_regression(feature, label, 0.5) # 3、保存最终的模型 diff --git a/README.md b/README.md index a521af4..5520332 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,12 @@ # Python机器学习算法 +写书不易，写一本好书更加不容易，本书上市以来收到了很多读书的赞赏。在本书的出版过程中，由于时间，精力等其他一些因素，虽经过多次修改，但仍然存在错误，谢谢各位读者能够不吝指出，下面是本书的勘误： + +[勘误地址](http://blog.csdn.net/google19890102/article/details/77996085) + ![这里写图片描述](http://img10.360buyimg.com/n1/jfs/t6391/74/1115083732/144183/2a82437f/594b5bb8Na3c6dfd4.jpg) -程序代码是《Python机器学习算法》的示例代码，目前该书在各大商城已经可以预定： +程序代码是《Python机器学习算法》的示例代码，目前该书在各大商城已经可以购买： - [京东](https://item.jd.com/12109305.html) - [当当](http://product.dangdang.com/25100931.html)