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1、决策树算法是一种非参数的决策算法，它根据数据的不同特征进行多层次的分类和判断，最终决策出所需要预测的结果。 它既可以解决分类算法，也可以解决回归问题 ，具有很好的解释能力。另外，对于决策树的构建方法具有多种出发点，它具有多种构建方式，如何构建决策树的出发点主要在于决策树每一个决策点上需要 在哪些维度上进行划分以及在这些维度的哪些阈值节点做划分 等细节问题。 具体在sklearn中调用决策树算法解决分类问题和回归问题的程序代码如下所示： #1-1导入基础训练数据集import numpy as npimport matplotlib.pyplot as pltfrom sklearn import datasetsd=datasets.load_iris()x=d.data[:,2:]y=d.targetplt.figure()plt.scatter(x[y==0,0],x[y==0,1],color="r")plt.scatter(x[y==1,0],x[y==1,1],color="g")plt.scatter(x[y==2,0],x[y==2,1],color="b")plt.show()#1-2导入sklearn中的决策树算法进行数据的分类问题实现训练预测from sklearn.tree import DecisionTreeClassifierdt1

Maybe it looks silly to ask this but I am confused. I referred to Configuring Log4j property but it doesn't seem to help. I have written a simple web service HelloWorld. And while running it I am getting the error something like this : log4j:WARN No appenders could be found for logger (org.apache.axis.transport.http.AxisServlet). log4j:WARN Please initialize the log4j system properly. I am not sure why its happening. I am generating the web-service using Eclipse and deployed in Tomcat 6.0. I check on Axis Developer's Guide and according to them log4j.configuration=log4j.properties Use this

This is the error that I got when I created my first Axis2 web service using Eclipse. After I wrote the class, I created the web service with Apache Axis2. When I click the start server button in eclipse it gives an error message: Could not publish server configuration for Tomcat v6.0 Server at localhost. Multiple contexts have a path of "/FirstApache". FirstApache is the dynamic web project that I created before. I selected the correct web project from the configuration part in the web service wizard. How can I fix this? Search for the server.xml file and check your <Context> tags, probably

I've been trying to make a 4 sided graph / 4 sided grid that can show and connect points that I set However using the Chart in the toolbox did not work as I couldn't find that type of a graph. How can I make one? Example graph: This is quite simple. All you need to do is tell the Chart to place the Crossing of the Axis at certain points, instead of keeping it unset ( NaN ). You also should set the range by setting Minimum and Maximum : ChartArea CA = chart1.ChartAreas[0]; Series S1 = chart1.Series[0]; S1.ChartType = SeriesChartType.Line; CA.AxisX.Maximum = 100; CA.AxisX.Minimum = -100; CA

1、支撑向量机SVM是一种非常重要和广泛的机器学习算法，它的算法出发点是尽可能找到最优的决策边界，使得模型的泛化能力尽可能地好，因此SVM对未来数据的预测也是更加准确的。 在sklearn章调用SVM算法的代码实现如下所示： #（一）sklearn中利用SVM算法解决分类问题 import numpy as npimport matplotlib.pyplot as pltfrom sklearn import datasetsd=datasets.load_iris()x=d.datay=d.targetx=x[y<2,:2]y=y[y<2]print(x)print(y)plt.figure()plt.scatter(x[y==0,0],x[y==0,1],color="r")plt.scatter(x[y==1,0],x[y==1,1],color="g")plt.show()#进行数据据标准化处理（线性方式）from sklearn.preprocessing import StandardScalers1=StandardScaler()s1.fit(x)x_standard=s1.transform(x)print(np.hstack([x,x_standard]))#导入sklearn中SVM的线性分类算法LinearSVCfrom sklearn.svm

Numpy特点 Numpy作为使用Python进行科学计算的常用库，有着如下特点： 提供了N维数组（矩阵），快速高效，矢量数学运算； 高效的Index，不需要循环，因为底层实现采用了C语言开发。 常见的数组和矩阵的方法 数组和矩阵的创建与维度信息 numpy.array() ## 数组的创建 vector = numpy.array([1,2,3,4]) ## 矩阵的创建 matrix = numpy.array([ [1,2,3], [4,5,6], [7,8,9] ]) shape ## 打印数组的维度信息 vector.shape() ——》(4,) # 数组中存在4个元素 ## 打印矩阵的维度信息 matrix.shape()——》(3,3) #三行三列 reshape eg: a = np.arange(15).reshape(3, 5) #随机创建3行5列的矩阵 Out: [[ 0 1 2 3 4] [ 5 6 7 8 9] [10 11 12 13 14]] a.ndim # 返回其维数 即 2 注意： reshape可以创建一个改变了尺寸的新数组，但是原始数组的shape是不会发生变化的。 reshape(-1) :数组新的shape属性应该要与原来的配套，如果等于-1的话，那么Numpy会根据剩下的维度计算出数组的另外一个shape属性值。 eg： z = np

I am trying to create a matlab plot with multiple x-axis one below another and just one y-axis. I have looked through the Mathworks file exchange and there are only suggestions/scripts for multiple y-axis. I would like to achieve something like this question for R . johan Here is an example solution if you only need a second axis for showing a different scale (Jeff_K's solution but more worked out): first_axis = gca; sqz = 0.12; %// distance to squeeze the first plot set(first_axis, 'Position', get(first_axis, 'Position') + [0 sqz 0 -sqz ]); ax2 = axes('Position', get(first_axis, 'Position') .

I have a d3 bar chart whose values range from 0-3. I would like the y-axis to only show integer values, which I can do by var yAxis = d3.svg.axis().scale(y).orient("right").tickFormat(d3.format("d")); However, there are still tick marks at the non-integer markings. Setting the tick format only hides those labels. I can explicitly set the number of ticks or the tick values, but what I'd like to do is to just be able to specify that tick marks only appear at integer values. Is that possible? You could add .ticks(4) and .tickSubdivide(0) as I've done below: var yAxis = d3.svg.axis() .scale(y)

I'm trying to write a web service client in c# which the webservice is Java Axis 1.4. Axis service requires the Authorization: Basic Base64EncodedToken header value in the HTTP Headers. I can't find a way to set this header in standart ways of consuming web services in visual studio.net, like normal WSDL generated refernce nor with WSE3.0 I can't use WCF as the project is developed using .net 2.0. Is there any way to do this ? user334291 It seems the original author has found their solution, but for anyone else who gets here looking to add actual custom headers, if you have access to mod the

1. import pandas as pd import numpy as np df1 = pd.DataFrame(np.arange(12).reshape(3, 4)) df2 = pd.DataFrame(np.arange(20).reshape(4, 5)) print(df1) print(df2) print(df1.add(df2, fill_value=1)) # 加运算 fill_value指定填充值,未对齐的数据将和填充值做运算 print(df1.sub(df2, fill_value=1)) # 减运算 fill_value指定填充值,未对齐的数据将和填充值做运算 print(df1.mul(df2, fill_value=1)) # 乘运算 fill_value指定填充值,未对齐的数据将和填充值做运算 print(df1.div(df2)) # 除运算 fill_value指定填充值,未对齐的数据将和填充值做运算 print(df1.truediv(df2)) # 除运算 fill_value指定填充值,未对齐的数据将和填充值做运算 print(df1.floordiv(df2)) # 向下取整除法 fill_value指定填充值,未对齐的数据将和填充值做运算 print(df1.mod(df2)) # 模运算 fill_value指定填充值