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机器学习
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# 面向对象的绘图方式
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## 配置参数
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* axex: 设置坐标轴边界和表面的颜色、坐标刻度值大小和网格的显示
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* figure: 控制dpi、边界颜色、图形大小、和子区( subplot)设置
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* font: 字体集（font family）、字体大小和样式设置
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* grid: 设置网格颜色和线性
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* legend: 设置图例和其中的文本的显示
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* line: 设置线条（颜色、线型、宽度等）和标记
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* patch: 是填充2D空间的图形对象，如多边形和圆。控制线宽、颜色和抗锯齿设置等。
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* savefig: 可以对保存的图形进行单独设置。例如，设置渲染的文件的背景为白色。
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* verbose: 设置matplotlib在执行期间信息输出，如silent、helpful、debug和debug-annoying。
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* xticks和yticks: 为x,y轴的主刻度和次刻度设置颜色、大小、方向，以及标签大小。
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## 线条风格
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线条风格linestyle或ls | 描述
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----|---
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‘-‘ |实线
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‘:’ |虚线
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‘–’ |破折线
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‘None’,’ ‘,’’ |什么都不画
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‘-.’ |点划线
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## 线条标记
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标记maker | 描述
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----|----
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‘o’ |圆圈
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‘.’ |点
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‘D’ |菱形
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‘s’ |正方形
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‘h’ |六边形1
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‘*’ |星号
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‘H’ |六边形2
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‘d’ |小菱形
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‘_’ | 水平线
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‘v’ |一角朝下的三角形
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‘8’ |八边形
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‘<’ | 一角朝左的三角形
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‘p’ |五边形
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‘>’ |一角朝右的三角形
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‘,’ |像素
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‘^’ | 一角朝上的三角形
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‘+’ | 加号
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‘\ ‘ |竖线
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‘None’,’’,’ ‘ |无
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‘x’ | X
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## 颜色
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别名 | 颜色
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---|---
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b | 蓝色
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g |绿色
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r |红色
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y |黄色
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c |青色
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k |黑色
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m |洋红色
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w |白色
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## 绘图步骤
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```py
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#使用numpy产生数据
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x=np.arange(-5,5,0.1)
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y=x*3
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#创建窗口、子图
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#方法1：先创建窗口，再创建子图。（一定绘制）
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fig = plt.figure(num=1, figsize=(15, 8),dpi=80) #开启一个窗口，同时设置大小，分辨率
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ax1 = fig.add_subplot(2,1,1) #通过fig添加子图，参数：行数，列数，第几个。
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ax2 = fig.add_subplot(2,1,2) #通过fig添加子图，参数：行数，列数，第几个。
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print(fig,ax1,ax2)
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#方法2：一次性创建窗口和多个子图。（空白不绘制）
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fig,axarr = plt.subplots(4,1) #开一个新窗口，并添加4个子图，返回子图数组
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ax1 = axarr[0] #通过子图数组获取一个子图
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print(fig,ax1)
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#方法3：一次性创建窗口和一个子图。（空白不绘制）
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ax1 = plt.subplot(1,1,1,facecolor='white') #开一个新窗口，创建1个子图。facecolor设置背景颜色
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print(ax1)
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#获取对窗口的引用，适用于上面三种方法
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# fig = plt.gcf() #获得当前figure
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# fig=ax1.figure #获得指定子图所属窗口
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# fig.subplots_adjust(left=0) #设置窗口左内边距为0，即左边留白为0。
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#设置子图的基本元素
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ax1.set_title('python-drawing') #设置图体，plt.title
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ax1.set_xlabel('x-name') #设置x轴名称,plt.xlabel
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ax1.set_ylabel('y-name') #设置y轴名称,plt.ylabel
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plt.axis([-6,6,-10,10]) #设置横纵坐标轴范围，这个在子图中被分解为下面两个函数
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ax1.set_xlim(-5,5) #设置横轴范围，会覆盖上面的横坐标,plt.xlim
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ax1.set_ylim(-10,10) #设置纵轴范围，会覆盖上面的纵坐标,plt.ylim
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xmajorLocator = MultipleLocator(2) #定义横向主刻度标签的刻度差为2的倍数。就是隔几个刻度才显示一个标签文本
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ymajorLocator = MultipleLocator(3) #定义纵向主刻度标签的刻度差为3的倍数。就是隔几个刻度才显示一个标签文本
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ax1.xaxis.set_major_locator(xmajorLocator) #x轴 应用定义的横向主刻度格式。如果不应用将采用默认刻度格式
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ax1.yaxis.set_major_locator(ymajorLocator) #y轴 应用定义的纵向主刻度格式。如果不应用将采用默认刻度格式
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ax1.xaxis.grid(True, which='major') #x坐标轴的网格使用定义的主刻度格式
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ax1.yaxis.grid(True, which='major') #x坐标轴的网格使用定义的主刻度格式
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ax1.set_xticks([]) #去除坐标轴刻度
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ax1.set_xticks((-5,-3,-1,1,3,5)) #设置坐标轴刻度
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ax1.set_xticklabels(labels=['x1','x2','x3','x4','x5'],rotation=-30,fontsize='small') #设置刻度的显示文本，rotation旋转角度，fontsize字体大小
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plot1=ax1.plot(x,y,marker='o',color='g',label='legend1') #点图：marker图标
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plot2=ax1.plot(x,y,linestyle='--',alpha=0.5,color='r',label='legend2') #线图：linestyle线性，alpha透明度，color颜色，label图例文本
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ax1.legend(loc='upper left') #显示图例,plt.legend()
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ax1.text(2.8, 7, r'y=3*x') #指定位置显示文字,plt.text()
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ax1.annotate('important point', xy=(2, 6), xytext=(3, 1.5), #添加标注，参数：注释文本、指向点、文字位置、箭头属性
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arrowprops=dict(facecolor='black', shrink=0.05),
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)
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#显示网格。which参数的值为major(只绘制大刻度)、minor(只绘制小刻度)、both，默认值为major。axis为'x','y','both'
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ax1.grid(b=True,which='major',axis='both',alpha= 0.5,color='skyblue',linestyle='--',linewidth=2)
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axes1 = plt.axes([.2, .3, .1, .1], facecolor='y') #在当前窗口添加一个子图，rect=[左, 下, 宽, 高]，是使用的绝对布局，不和以存在窗口挤占空间
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axes1.plot(x,y) #在子图上画图
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plt.savefig('aa.jpg',dpi=400,bbox_inches='tight') #savefig保存图片，dpi分辨率，bbox_inches子图周边白色空间的大小
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plt.show() #打开窗口，对于方法1创建在窗口一定绘制，对于方法2方法3创建的窗口，若坐标系全部空白，则不绘制
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```
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## plot属性
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```py
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属性 值类型
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alpha 浮点值
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animated [True / False]
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antialiased or aa [True / False]
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clip_box matplotlib.transform.Bbox 实例
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clip_on [True / False]
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clip_path Path 实例， Transform，以及Patch实例
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color or c 任何 matplotlib 颜色
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contains 命中测试函数
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dash_capstyle ['butt' / 'round' / 'projecting']
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dash_joinstyle ['miter' / 'round' / 'bevel']
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dashes 以点为单位的连接/断开墨水序列
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data (np.array xdata, np.array ydata)
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figure matplotlib.figure.Figure 实例
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label 任何字符串
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linestyle or ls [ '-' / '--' / '-.' / ':' / 'steps' / ...]
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linewidth or lw 以点为单位的浮点值
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lod [True / False]
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marker [ '+' / ',' / '.' / '1' / '2' / '3' / '4' ]
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markeredgecolor or mec 任何 matplotlib 颜色
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markeredgewidth or mew 以点为单位的浮点值
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markerfacecolor or mfc 任何 matplotlib 颜色
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markersize or ms 浮点值
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markevery [ None / 整数值 / (startind, stride) ]
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picker 用于交互式线条选择
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pickradius 线条的拾取选择半径
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solid_capstyle ['butt' / 'round' / 'projecting']
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solid_joinstyle ['miter' / 'round' / 'bevel']
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transform matplotlib.transforms.Transform 实例
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visible [True / False]
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xdata np.array
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ydata np.array
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zorder 任何数值
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```
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## 多图绘制
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```py
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#一个窗口，多个图，多条数据
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sub1=plt.subplot(211,facecolor=(0.1843,0.3098,0.3098)) #将窗口分成2行1列，在第1个作图，并设置背景色
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sub2=plt.subplot(212) #将窗口分成2行1列，在第2个作图
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sub1.plot(x,y) #绘制子图
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sub2.plot(x,y) #绘制子图
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axes1 = plt.axes([.2, .3, .1, .1], facecolor='y') #添加一个子坐标系，rect=[左, 下, 宽, 高]
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plt.plot(x,y) #绘制子坐标系，
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axes2 = plt.axes([0.7, .2, .1, .1], facecolor='y') #添加一个子坐标系，rect=[左, 下, 宽, 高]
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plt.plot(x,y)
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plt.show()
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```
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## 极坐标
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```py
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fig = plt.figure(2) #新开一个窗口
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ax1 = fig.add_subplot(1,2,1,polar=True) #启动一个极坐标子图
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theta=np.arange(0,2*np.pi,0.02) #角度数列值
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ax1.plot(theta,2*np.ones_like(theta),lw=2) #画图，参数：角度，半径，lw线宽
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ax1.plot(theta,theta/6,linestyle='--',lw=2) #画图，参数：角度，半径，linestyle样式，lw线宽
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ax2 = fig.add_subplot(1,2,2,polar=True) #启动一个极坐标子图
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ax2.plot(theta,np.cos(5*theta),linestyle='--',lw=2)
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ax2.plot(theta,2*np.cos(4*theta),lw=2)
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ax2.set_rgrids(np.arange(0.2,2,0.2),angle=45) #距离网格轴，轴线刻度和显示位置
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ax2.set_thetagrids([0,45,90]) #角度网格轴，范围0-360度
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plt.show()
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```
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## 柱状图
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```py
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plt.figure(3)
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x_index = np.arange(5) #柱的索引
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x_data = ('A', 'B', 'C', 'D', 'E')
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y1_data = (20, 35, 30, 35, 27)
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y2_data = (25, 32, 34, 20, 25)
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bar_width = 0.35 #定义一个数字代表每个独立柱的宽度
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rects1 = plt.bar(x_index, y1_data, width=bar_width,alpha=0.4, color='b',label='legend1') #参数：左偏移、高度、柱宽、透明度、颜色、图例
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rects2 = plt.bar(x_index + bar_width, y2_data, width=bar_width,alpha=0.5,color='r',label='legend2') #参数：左偏移、高度、柱宽、透明度、颜色、图例
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#关于左偏移，不用关心每根柱的中心不中心，因为只要把刻度线设置在柱的中间就可以了
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plt.xticks(x_index + bar_width/2, x_data) #x轴刻度线
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plt.legend() #显示图例
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plt.tight_layout() #自动控制图像外部边缘，此方法不能够很好的控制图像间的间隔
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plt.show()
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```
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## 直方图
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```py
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fig,(ax0,ax1) = plt.subplots(nrows=2,figsize=(9,6)) #在窗口上添加2个子图
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sigma = 1 #标准差
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mean = 0 #均值
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x=mean+sigma*np.random.randn(10000) #正态分布随机数
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ax0.hist(x,bins=40,normed=False,histtype='bar',facecolor='yellowgreen',alpha=0.75) #normed是否归一化，histtype直方图类型，facecolor颜色，alpha透明度
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ax1.hist(x,bins=20,normed=1,histtype='bar',facecolor='pink',alpha=0.75,cumulative=True,rwidth=0.8) #bins柱子的个数,cumulative是否计算累加分布，rwidth柱子宽度
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plt.show() #所有窗口运行
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```
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## 散点图
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```py
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fig = plt.figure(4) #添加一个窗口
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ax =fig.add_subplot(1,1,1) #在窗口上添加一个子图
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x=np.random.random(100) #产生随机数组
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y=np.random.random(100) #产生随机数组
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ax.scatter(x,y,s=x*1000,c='y',marker=(5,1),alpha=0.5,lw=2,facecolors='none') #x横坐标，y纵坐标，s图像大小，c颜色，marker图片，lw图像边框宽度
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plt.show() #所有窗口运行
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```
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## 三维图
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```py
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fig = plt.figure(5)
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ax=fig.add_subplot(1,1,1,projection='3d') #绘制三维图
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x,y=np.mgrid[-2:2:20j,-2:2:20j] #获取x轴数据，y轴数据
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z=x*np.exp(-x**2-y**2) #获取z轴数据
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ax.plot_surface(x,y,z,rstride=2,cstride=1,cmap=plt.cm.coolwarm,alpha=0.8) #绘制三维图表面
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ax.set_xlabel('x-name') #x轴名称
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ax.set_ylabel('y-name') #y轴名称
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ax.set_zlabel('z-name') #z轴名称
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plt.show()
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```
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## 集合图形
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```py
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fig = plt.figure(6) #创建一个窗口
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ax=fig.add_subplot(1,1,1) #添加一个子图
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rect1 = plt.Rectangle((0.1,0.2),0.2,0.3,color='r') #创建一个矩形，参数：(x,y),width,height
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circ1 = plt.Circle((0.7,0.2),0.15,color='r',alpha=0.3) #创建一个椭圆，参数：中心点，半径，默认这个圆形会跟随窗口大小进行长宽压缩
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pgon1 = plt.Polygon([[0.45,0.45],[0.65,0.6],[0.2,0.6]]) #创建一个多边形，参数：每个顶点坐标
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ax.add_patch(rect1) #将形状添加到子图上
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ax.add_patch(circ1) #将形状添加到子图上
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ax.add_patch(pgon1) #将形状添加到子图上
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fig.canvas.draw() #子图绘制
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plt.show()
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```
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359
Python/matplotlab/学习教程/02面向对象绘图实例.md
Normal file
359
Python/matplotlab/学习教程/02面向对象绘图实例.md
Normal file
@@ -0,0 +1,359 @@
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# 面向对象的绘图方式
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> 在这里的实例中，面向对象的方法与命令行的方式进行了混用，不提倡。首先运用好交互式命令行的方式进行绘图。
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## 配置参数
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* axex: 设置坐标轴边界和表面的颜色、坐标刻度值大小和网格的显示
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* figure: 控制dpi、边界颜色、图形大小、和子区( subplot)设置
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* font: 字体集（font family）、字体大小和样式设置
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* grid: 设置网格颜色和线性
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* legend: 设置图例和其中的文本的显示
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* line: 设置线条（颜色、线型、宽度等）和标记
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* patch: 是填充2D空间的图形对象，如多边形和圆。控制线宽、颜色和抗锯齿设置等。
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||||
* savefig: 可以对保存的图形进行单独设置。例如，设置渲染的文件的背景为白色。
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||||
* verbose: 设置matplotlib在执行期间信息输出，如silent、helpful、debug和debug-annoying。
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||||
* xticks和yticks: 为x,y轴的主刻度和次刻度设置颜色、大小、方向，以及标签大小。
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## 线条风格
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||||
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线条风格linestyle或ls | 描述
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||||
----|---
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||||
‘-‘ |实线
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||||
‘:’ |虚线
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||||
‘–’ |破折线
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‘None’,’ ‘,’’ |什么都不画
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‘-.’ |点划线
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||||
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## 线条标记
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||||
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标记maker | 描述
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||||
----|----
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||||
‘o’ |圆圈
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||||
‘.’ |点
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||||
‘D’ |菱形
|
||||
‘s’ |正方形
|
||||
‘h’ |六边形1
|
||||
‘*’ |星号
|
||||
‘H’ |六边形2
|
||||
‘d’ |小菱形
|
||||
‘_’ | 水平线
|
||||
‘v’ |一角朝下的三角形
|
||||
‘8’ |八边形
|
||||
‘<’ | 一角朝左的三角形
|
||||
‘p’ |五边形
|
||||
‘>’ |一角朝右的三角形
|
||||
‘,’ |像素
|
||||
‘^’ | 一角朝上的三角形
|
||||
‘+’ | 加号
|
||||
‘\ ‘ |竖线
|
||||
‘None’,’’,’ ‘ |无
|
||||
‘x’ | X
|
||||
|
||||
|
||||
## 颜色
|
||||
|
||||
别名 | 颜色
|
||||
---|---
|
||||
b | 蓝色
|
||||
g |绿色
|
||||
r |红色
|
||||
y |黄色
|
||||
c |青色
|
||||
k |黑色
|
||||
m |洋红色
|
||||
w |白色
|
||||
|
||||
|
||||
## 绘图步骤
|
||||
|
||||
```py
|
||||
#使用numpy产生数据
|
||||
x=np.arange(-5,5,0.1)
|
||||
y=x*3
|
||||
|
||||
#创建窗口、子图
|
||||
#方法1：先创建窗口，再创建子图。（一定绘制）
|
||||
fig = plt.figure(num=1, figsize=(15, 8),dpi=80)
|
||||
#开启一个窗口，同时设置大小，分辨率
|
||||
ax1 = fig.add_subplot(2,1,1)
|
||||
#通过fig添加子图，参数：行数，列数，第几个。
|
||||
ax2 = fig.add_subplot(2,1,2)
|
||||
#通过fig添加子图，参数：行数，列数，第几个。
|
||||
print(fig,ax1,ax2)
|
||||
#方法2：一次性创建窗口和多个子图。（空白不绘制）
|
||||
fig,axarr = plt.subplots(4,1)
|
||||
#开一个新窗口，并添加4个子图，返回子图数组
|
||||
ax1 = axarr[0]
|
||||
#通过子图数组获取一个子图
|
||||
print(fig,ax1)
|
||||
#方法3：一次性创建窗口和一个子图。（空白不绘制）
|
||||
ax1 = plt.subplot(1,1,1,facecolor='white')
|
||||
|
||||
#开一个新窗口，创建1个子图。facecolor设置背景颜色
|
||||
print(ax1)
|
||||
#获取对窗口的引用，适用于上面三种方法
|
||||
# fig = plt.gcf() #获得当前figure
|
||||
# fig=ax1.figure #获得指定子图所属窗口
|
||||
|
||||
# fig.subplots_adjust(left=0)
|
||||
#设置窗口左内边距为0，即左边留白为0。
|
||||
|
||||
#设置子图的基本元素
|
||||
ax1.set_title('python-drawing')
|
||||
#设置图体，plt.title
|
||||
ax1.set_xlabel('x-name')
|
||||
#设置x轴名称,plt.xlabel
|
||||
ax1.set_ylabel('y-name')
|
||||
#设置y轴名称,plt.ylabel
|
||||
plt.axis([-6,6,-10,10])
|
||||
#设置横纵坐标轴范围，这个在子图中被分解为下面两个函数
|
||||
ax1.set_xlim(-5,5)
|
||||
#设置横轴范围，会覆盖上面的横坐标,plt.xlim
|
||||
ax1.set_ylim(-10,10)
|
||||
#设置纵轴范围，会覆盖上面的纵坐标,plt.ylim
|
||||
|
||||
xmajorLocator = MultipleLocator(2)
|
||||
#定义横向主刻度标签的刻度差为2的倍数。就是隔几个刻度才显示一个标签文本
|
||||
ymajorLocator = MultipleLocator(3)
|
||||
#定义纵向主刻度标签的刻度差为3的倍数。就是隔几个刻度才显示一个标签文本
|
||||
|
||||
ax1.xaxis.set_major_locator(xmajorLocator)
|
||||
#x轴 应用定义的横向主刻度格式。如果不应用将采用默认刻度格式
|
||||
ax1.yaxis.set_major_locator(ymajorLocator)
|
||||
#y轴 应用定义的纵向主刻度格式。如果不应用将采用默认刻度格式
|
||||
|
||||
ax1.xaxis.grid(True, which='major')
|
||||
#x坐标轴的网格使用定义的主刻度格式
|
||||
ax1.yaxis.grid(True, which='major')
|
||||
#x坐标轴的网格使用定义的主刻度格式
|
||||
|
||||
ax1.set_xticks([])
|
||||
#去除坐标轴刻度
|
||||
ax1.set_xticks((-5,-3,-1,1,3,5))
|
||||
#设置坐标轴刻度
|
||||
ax1.set_xticklabels(labels=['x1','x2','x3','x4','x5'],rotation=-30,fontsize='small')
|
||||
#设置刻度的显示文本，rotation旋转角度，fontsize字体大小
|
||||
|
||||
plot1=ax1.plot(x,y,marker='o',color='g',label='legend1')
|
||||
#点图：marker图标
|
||||
plot2=ax1.plot(x,y,linestyle='--',alpha=0.5,color='r',label='legend2')
|
||||
#线图：linestyle线性，alpha透明度，color颜色，label图例文本
|
||||
|
||||
ax1.legend(loc='upper left')
|
||||
#显示图例,plt.legend()
|
||||
ax1.text(2.8, 7, r'y=3*x')
|
||||
#指定位置显示文字,plt.text()
|
||||
ax1.annotate('important point', xy=(2, 6), xytext=(3, 1.5), arrowprops=dict(facecolor='black', shrink=0.05),)
|
||||
#添加标注，参数：注释文本、指向点、文字位置、箭头属性
|
||||
#显示网格。which参数的值为major(只绘制大刻度)、minor(只绘制小刻度)、both，默认值为major。axis为'x','y','both'
|
||||
ax1.grid(b=True,which='major',axis='both',alpha= 0.5,color='skyblue',linestyle='--',linewidth=2)
|
||||
|
||||
axes1 = plt.axes([.2, .3, .1, .1], facecolor='y')
|
||||
#在当前窗口添加一个子图，rect=[左, 下, 宽, 高]，是使用的绝对布局，不和以存在窗口挤占空间
|
||||
axes1.plot(x,y)
|
||||
#在子图上画图
|
||||
plt.savefig('aa.jpg',dpi=400,bbox_inches='tight')
|
||||
#savefig保存图片，dpi分辨率，bbox_inches子图周边白色空间的大小
|
||||
plt.show()
|
||||
#打开窗口，对于方法1创建在窗口一定绘制，对于方法2方法3创建的窗口，若坐标系全部空白，则不绘制
|
||||
|
||||
```
|
||||
|
||||
## plot属性
|
||||
|
||||
```py
|
||||
属性 值类型
|
||||
alpha 浮点值
|
||||
animated [True / False]
|
||||
antialiased or aa [True / False]
|
||||
clip_box matplotlib.transform.Bbox 实例
|
||||
clip_on [True / False]
|
||||
clip_path Path 实例， Transform，以及Patch实例
|
||||
color or c 任何 matplotlib 颜色
|
||||
contains 命中测试函数
|
||||
dash_capstyle ['butt' / 'round' / 'projecting']
|
||||
dash_joinstyle ['miter' / 'round' / 'bevel']
|
||||
dashes 以点为单位的连接/断开墨水序列
|
||||
data (np.array xdata, np.array ydata)
|
||||
figure matplotlib.figure.Figure 实例
|
||||
label 任何字符串
|
||||
linestyle or ls [ '-' / '--' / '-.' / ':' / 'steps' / ...]
|
||||
linewidth or lw 以点为单位的浮点值
|
||||
lod [True / False]
|
||||
marker [ '+' / ',' / '.' / '1' / '2' / '3' / '4' ]
|
||||
markeredgecolor or mec 任何 matplotlib 颜色
|
||||
markeredgewidth or mew 以点为单位的浮点值
|
||||
markerfacecolor or mfc 任何 matplotlib 颜色
|
||||
markersize or ms 浮点值
|
||||
markevery [ None / 整数值 / (startind, stride) ]
|
||||
picker 用于交互式线条选择
|
||||
pickradius 线条的拾取选择半径
|
||||
solid_capstyle ['butt' / 'round' / 'projecting']
|
||||
solid_joinstyle ['miter' / 'round' / 'bevel']
|
||||
transform matplotlib.transforms.Transform 实例
|
||||
visible [True / False]
|
||||
xdata np.array
|
||||
ydata np.array
|
||||
zorder 任何数值
|
||||
```
|
||||
|
||||
## 多图绘制
|
||||
|
||||
```py
|
||||
#一个窗口，多个图，多条数据
|
||||
sub1=plt.subplot(211,facecolor=(0.1843,0.3098,0.3098))
|
||||
#将窗口分成2行1列，在第1个作图，并设置背景色
|
||||
sub2=plt.subplot(212)
|
||||
#将窗口分成2行1列，在第2个作图
|
||||
sub1.plot(x,y)
|
||||
#绘制子图
|
||||
sub2.plot(x,y)
|
||||
#绘制子图
|
||||
|
||||
axes1 = plt.axes([.2, .3, .1, .1], facecolor='y')
|
||||
#添加一个子坐标系，rect=[左, 下, 宽, 高]
|
||||
plt.plot(x,y)
|
||||
#绘制子坐标系，
|
||||
axes2 = plt.axes([0.7, .2, .1, .1], facecolor='y')
|
||||
#添加一个子坐标系，rect=[左, 下, 宽, 高]
|
||||
plt.plot(x,y)
|
||||
plt.show()
|
||||
```
|
||||
## 极坐标
|
||||
|
||||
```py
|
||||
fig = plt.figure(2)
|
||||
#新开一个窗口
|
||||
ax1 = fig.add_subplot(1,2,1,polar=True)
|
||||
#启动一个极坐标子图
|
||||
theta=np.arange(0,2*np.pi,0.02)
|
||||
#角度数列值
|
||||
ax1.plot(theta,2*np.ones_like(theta),lw=2)
|
||||
#画图，参数：角度，半径，lw线宽
|
||||
ax1.plot(theta,theta/6,linestyle='--',lw=2)
|
||||
#画图，参数：角度，半径，linestyle样式，lw线宽
|
||||
|
||||
ax2 = fig.add_subplot(1,2,2,polar=True)
|
||||
#启动一个极坐标子图
|
||||
ax2.plot(theta,np.cos(5*theta),linestyle='--',lw=2)
|
||||
ax2.plot(theta,2*np.cos(4*theta),lw=2)
|
||||
|
||||
ax2.set_rgrids(np.arange(0.2,2,0.2),angle=45) #距离网格轴，轴线刻度和显示位置
|
||||
ax2.set_thetagrids([0,45,90]) #角度网格轴，范围0-360度
|
||||
|
||||
plt.show()
|
||||
```
|
||||
|
||||
## 柱状图
|
||||
|
||||
```py
|
||||
plt.figure(3)
|
||||
x_index = np.arange(5)
|
||||
#柱的索引
|
||||
x_data = ('A', 'B', 'C', 'D', 'E')
|
||||
y1_data = (20, 35, 30, 35, 27)
|
||||
y2_data = (25, 32, 34, 20, 25)
|
||||
bar_width = 0.35
|
||||
#定义一个数字代表每个独立柱的宽度
|
||||
|
||||
rects1 = plt.bar(x_index, y1_data, width=bar_width,alpha=0.4, color='b',label='legend1')
|
||||
#参数：左偏移、高度、柱宽、透明度、颜色、图例
|
||||
rects2 = plt.bar(x_index + bar_width, y2_data, width=bar_width,alpha=0.5,color='r',label='legend2')
|
||||
#参数：左偏移、高度、柱宽、透明度、颜色、图例
|
||||
#关于左偏移，不用关心每根柱的中心不中心，因为只要把刻度线设置在柱的中间就可以了
|
||||
plt.xticks(x_index + bar_width/2, x_data)
|
||||
#x轴刻度线
|
||||
plt.legend()
|
||||
#显示图例
|
||||
plt.tight_layout()
|
||||
#自动控制图像外部边缘，此方法不能够很好的控制图像间的间隔
|
||||
plt.show()
|
||||
```
|
||||
|
||||
## 直方图
|
||||
|
||||
```py
|
||||
fig,(ax0,ax1) = plt.subplots(nrows=2,figsize=(9,6))
|
||||
#在窗口上添加2个子图
|
||||
sigma = 1
|
||||
#标准差
|
||||
mean = 0
|
||||
#均值
|
||||
x=mean+sigma*np.random.randn(10000)
|
||||
#正态分布随机数
|
||||
ax0.hist(x,bins=40,normed=False,histtype='bar',facecolor='yellowgreen',alpha=0.75)
|
||||
#normed是否归一化，histtype直方图类型，facecolor颜色，alpha透明度
|
||||
ax1.hist(x,bins=20,normed=1,histtype='bar',facecolor='pink',alpha=0.75,cumulative=True,rwidth=0.8)
|
||||
#bins柱子的个数,cumulative是否计算累加分布，rwidth柱子宽度
|
||||
plt.show()
|
||||
#所有窗口运行
|
||||
```
|
||||
|
||||
## 散点图
|
||||
|
||||
```py
|
||||
fig = plt.figure(4)
|
||||
#添加一个窗口
|
||||
ax =fig.add_subplot(1,1,1)
|
||||
#在窗口上添加一个子图
|
||||
x=np.random.random(100)
|
||||
#产生随机数组
|
||||
y=np.random.random(100)
|
||||
#产生随机数组
|
||||
ax.scatter(x,y,s=x*1000,c='y',marker=(5,1),alpha=0.5,lw=2,facecolors='none')
|
||||
#x横坐标，y纵坐标，s图像大小，c颜色，marker图片，lw图像边框宽度
|
||||
plt.show()
|
||||
#所有窗口运行
|
||||
```
|
||||
|
||||
## 三维图
|
||||
|
||||
```py
|
||||
fig = plt.figure(5)
|
||||
ax=fig.add_subplot(1,1,1,projection='3d')
|
||||
#绘制三维图
|
||||
|
||||
x,y=np.mgrid[-2:2:20j,-2:2:20j]
|
||||
#获取x轴数据，y轴数据
|
||||
z=x*np.exp(-x**2-y**2)
|
||||
#获取z轴数据
|
||||
|
||||
ax.plot_surface(x,y,z,rstride=2,cstride=1,cmap=plt.cm.coolwarm,alpha=0.8)
|
||||
#绘制三维图表面
|
||||
ax.set_xlabel('x-name')
|
||||
#x轴名称
|
||||
ax.set_ylabel('y-name')
|
||||
#y轴名称
|
||||
ax.set_zlabel('z-name')
|
||||
#z轴名称
|
||||
|
||||
plt.show()
|
||||
```
|
||||
|
||||
## 集合图形
|
||||
|
||||
```py
|
||||
fig = plt.figure(6)
|
||||
#创建一个窗口
|
||||
ax=fig.add_subplot(1,1,1)
|
||||
#添加一个子图
|
||||
rect1 = plt.Rectangle((0.1,0.2),0.2,0.3,color='r')
|
||||
#创建一个矩形，参数：(x,y),width,height
|
||||
circ1 = plt.Circle((0.7,0.2),0.15,color='r',alpha=0.3)
|
||||
#创建一个椭圆，参数：中心点，半径，默认这个圆形会跟随窗口大小进行长宽压缩
|
||||
pgon1 = plt.Polygon([[0.45,0.45],[0.65,0.6],[0.2,0.6]])
|
||||
#创建一个多边形，参数：每个顶点坐标
|
||||
|
||||
ax.add_patch(rect1)
|
||||
#将形状添加到子图上
|
||||
ax.add_patch(circ1)
|
||||
#将形状添加到子图上
|
||||
ax.add_patch(pgon1)
|
||||
#将形状添加到子图上
|
||||
|
||||
fig.canvas.draw()
|
||||
#子图绘制
|
||||
plt.show()
|
||||
```
|
||||
135
Python/matplotlab/学习教程/03基本用法.md
Normal file
135
Python/matplotlab/学习教程/03基本用法.md
Normal file
@@ -0,0 +1,135 @@
|
||||
# 基本用法
|
||||
## 本章知识点归纳如下：
|
||||
|
||||
* 导入模块：import matplotlib.pyplot as plt
|
||||
* 定义图像窗口：plt.figure()
|
||||
* 画图：plt.plot(x, y)
|
||||
* 定义坐标轴范围：plt.xlim()/plt.ylim()
|
||||
* 定义坐标轴名称：plt.xlabel()/plt.ylabel()
|
||||
* 定义坐标轴刻度及名称：plt.xticks()/plt.yticks()
|
||||
* 设置图像边框颜色：ax = plt.gca() ax.spines[].set_color()
|
||||
* 调整刻度位置：ax.xaxis.set_ticks_position()/ax.yaxis.set_ticks_position()
|
||||
* 调整边框（坐标轴）位置：ax.spines[].set_position()
|
||||
|
||||
## 导入模块
|
||||
|
||||
* 使用import导入模块matplotlib.pyplot，并简写成plt；使用import导入模块numpy，并简写成np
|
||||
```py
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
```
|
||||
* 然后创建两组数据，使用np.linspace定义x：范围是(-3,3)，个数是50，将产生一组（-3，3）内均匀分布的50个数；(x,y1)表示曲线1，(x,y2)表示曲线2。
|
||||
```py
|
||||
x = np.linspace(-3, 3, 50)
|
||||
y1 = 2*x + 1
|
||||
y2 = x**2
|
||||
```
|
||||
|
||||
## 定义图像窗口并画图
|
||||
|
||||
* 在画图前使用plt.figure()定义一个图像窗口：编号为3；大小为(8, 5)；这两项参数可缺省。其中，num参数决定了程序运行后弹出的图像窗口名字，但在klab平台下不会显示。接着，我们使用plt.plot画出(x ,y2)曲线；使用plt.plot画(x ,y1)曲线，曲线的颜色属性(color)为红色；曲线的宽度(linewidth)为1.0；曲线的类型(linestyle)为虚线，除了虚线外，大家还可使用以下线性：'-'、'--'、'-.'、':' 。接着，我们使用plt.show()显示图像。
|
||||
|
||||
```py
|
||||
plt.figure(num=3, figsize=(8, 5))
|
||||
plt.plot(x, y2)
|
||||
plt.plot(x, y1, color='red', linewidth=1.0, linestyle='--')
|
||||
plt.show()
|
||||
```
|
||||
|
||||
## 定义坐标轴名称及范围
|
||||
* 使用plt.xlim设置x坐标轴范围：(-1, 2)； 使用plt.ylim设置y坐标轴范围：(-2, 3)； 使用plt.xlabel设置x坐标轴名称：’I am x’； 使用plt.ylabel设置y坐标轴名称：’I am y’；
|
||||
```py
|
||||
plt.figure(num=3, figsize=(8, 5),)
|
||||
plt.plot(x, y2)
|
||||
plt.plot(x, y1, color='red', linewidth=1.0, linestyle='--')
|
||||
plt.xlim((-1, 2))
|
||||
plt.ylim((-2, 3))
|
||||
plt.xlabel('I am x')
|
||||
plt.ylabel('I am y')
|
||||
plt.show()
|
||||
```
|
||||
## 定义坐标轴刻度及名称
|
||||
|
||||
* 有时候，我们的坐标轴刻度可能并不是一连串的数字，而是一些文字，或者我们想要调整坐标轴的刻度的稀疏，这时，就需要使用plt.xticks()或者plt.yticks()来进行调整：首先，使用np.linspace定义新刻度范围以及个数：范围是(-1,2);个数是5。使用plt.xticks设置x轴刻度：范围是(-1,2);个数是5。使用plt.yticks设置y轴刻度以及名称：刻度为[-2, -1.8, -1, 1.22, 3]；对应刻度的名称为[‘really bad’,’bad’,’normal’,’good’, ‘really good’]。使用plt.show()显示图像。
|
||||
```py
|
||||
plt.figure(num=3, figsize=(8, 5))
|
||||
plt.plot(x, y2)
|
||||
plt.plot(x, y1, color='red', linewidth=1.0, linestyle='--')
|
||||
plt.xlim((-1, 2))
|
||||
plt.ylim((-2, 3))
|
||||
plt.xlabel('I am x')
|
||||
plt.ylabel('I am y')
|
||||
new_ticks = np.linspace(-1, 2, 5)
|
||||
print(new_ticks)
|
||||
plt.xticks(new_ticks)
|
||||
plt.yticks([-2, -1.8, -1, 1.22, 3],[r'$really\ bad$', r'$bad$', r'$normal$', r'$good$', r'$really\ good$'])
|
||||
plt.show()
|
||||
[-1. -0.25 0.5 1.25 2. ]
|
||||
```
|
||||
## 设置图像边框颜色
|
||||
* 细心的小伙伴可能会注意到，我们的图像坐标轴总是由上下左右四条线组成，我们也可以对它们进行修改：首先，使用plt.gca()获取当前坐标轴信息。使用.spines设置边框；使用.set_color设置边框颜色；
|
||||
```py
|
||||
plt.figure(num=3, figsize=(8, 5))
|
||||
plt.plot(x, y2)
|
||||
plt.plot(x, y1, color='red', linewidth=1.0, linestyle='--')
|
||||
plt.xlim((-1, 2))
|
||||
plt.ylim((-2, 3))
|
||||
plt.xticks(new_ticks)
|
||||
plt.yticks([-2, -1.8, -1, 1.22, 3],[r'$really\ bad$', r'$bad$', r'$normal$', r'$good$', r'$really\ good$'])
|
||||
ax = plt.gca()
|
||||
ax.spines['right'].set_color('none')
|
||||
ax.spines['top'].set_color('none')
|
||||
plt.show()
|
||||
```
|
||||
调整刻度及边框位置
|
||||
使用.xaxis.set_ticks_position设置x坐标刻度数字或名称的位置：bottom.（所有位置：top，bottom，both，default，none）；使用.spines设置边框：x轴；使用.set_position设置边框位置：y=0的位置；（位置所有属性：outward，axes，data）
|
||||
|
||||
plt.figure(num=3, figsize=(8, 5))
|
||||
plt.plot(x, y2)
|
||||
plt.plot(x, y1, color='red', linewidth=1.0, linestyle='--')
|
||||
plt.xlim((-1, 2))
|
||||
plt.ylim((-2, 3))
|
||||
plt.xticks(new_ticks)
|
||||
plt.yticks([-2, -1.8, -1, 1.22, 3],[r'$really\ bad$', r'$bad$', r'$normal$', r'$good$', r'$really\ good$'])
|
||||
ax = plt.gca()
|
||||
ax.spines['right'].set_color('none')
|
||||
ax.spines['top'].set_color('none')
|
||||
ax.xaxis.set_ticks_position('bottom')
|
||||
ax.spines['bottom'].set_position(('data', 0))
|
||||
plt.show()
|
||||
|
||||
使用.yaxis.set_ticks_position设置y坐标刻度数字或名称的位置：left.（所有位置：left，right，both，default，none） 使用.spines设置边框：y轴；使用.set_position设置边框位置：x=0的位置；（位置所有属性：outward，axes，data） 使用plt.show显示图像.
|
||||
|
||||
plt.figure(num=3, figsize=(8, 5))
|
||||
plt.plot(x, y2)
|
||||
plt.plot(x, y1, color='red', linewidth=1.0, linestyle='--')
|
||||
plt.xlim((-1, 2))
|
||||
plt.ylim((-2, 3))
|
||||
plt.xticks(new_ticks)
|
||||
plt.yticks([-2, -1.8, -1, 1.22, 3],[r'$really\ bad$', r'$bad$', r'$normal$', r'$good$', r'$really\ good$'])
|
||||
ax = plt.gca()
|
||||
ax.spines['right'].set_color('none')
|
||||
ax.spines['top'].set_color('none')
|
||||
ax.xaxis.set_ticks_position('bottom')
|
||||
ax.spines['bottom'].set_position(('data', 0))
|
||||
ax.yaxis.set_ticks_position('left')
|
||||
ax.spines['left'].set_position(('data',0))
|
||||
plt.show()
|
||||
|
||||
练一练
|
||||
小伙伴们，以上就是matplotlib的基本用法，是不是比较简单呢？现在，请根据上述所学内容，画出直线 y = x-1, 线型为虚线，线宽为1，纵坐标范围（-2，1），横坐标范围（-1，2），横纵坐标在（0，0）坐标点相交。横坐标的 [-1,-0.5,1] 分别对应 [bad, normal, good]。请一定自己尝试一番再看下面的答案噢~
|
||||
|
||||
#答案
|
||||
x = np.linspace(-1, 2, 50)
|
||||
y = x - 1
|
||||
plt.figure()
|
||||
plt.plot(x,y, linewidth=1.0, linestyle='--')
|
||||
plt.xlim((-1,2))
|
||||
plt.ylim((-2,2))
|
||||
plt.xticks([-1,-0.5,1],['bad', 'normal', 'good'])
|
||||
ax = plt.gca()
|
||||
ax.spines['top'].set_color('none')
|
||||
ax.spines['right'].set_color('none')
|
||||
ax.spines['left'].set_position(('data',0))
|
||||
ax.spines['bottom'].set_position(('data',0))
|
||||
plt.show()
|
||||
33
Python/matplotlab/学习教程/test1.py
Normal file
33
Python/matplotlab/学习教程/test1.py
Normal file
@@ -0,0 +1,33 @@
|
||||
# changshi
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
# 定义数据集
|
||||
x = np.linspace(-3,3,50)
|
||||
y1 = 2*x+1
|
||||
y2 = x**2
|
||||
|
||||
|
||||
# 绘制图像
|
||||
plt.figure(num=3,figsize=(10,10))
|
||||
plt.plot(x,y1)
|
||||
plt.plot(x,y2,color='red',linewidth=1,linestyle='--')
|
||||
|
||||
# 定义坐标轴的范围及名称
|
||||
plt.xlim((-5,5))
|
||||
plt.ylim((0,10))
|
||||
|
||||
plt.xlabel('i am x')
|
||||
plt.ylabel('i am y')
|
||||
|
||||
# 刻度
|
||||
ticks = np.linspace(-1,2,5)
|
||||
plt.xticks(ticks)
|
||||
plt.yticks([-2,-1.8,-1,1.22,3],[r'$really\ bad$', r'$bad$', r'$normal$', r'$good$', r'$really\ good$'])
|
||||
|
||||
# 设置边框颜色
|
||||
ax = plt.gca()
|
||||
ax.spines['right'].set_color('none')
|
||||
ax.spines['top'].set_color('none')
|
||||
# 展示图像
|
||||
plt.show()
|
||||
703
Python/numpy/20随机数.md
Normal file
703
Python/numpy/20随机数.md
Normal file
@@ -0,0 +1,703 @@
|
||||
# 使用numpy产生随机数
|
||||
numpy中的random模块包含了很多方法可以用来产生随机数，这篇文章将对random中的一些常用方法做一个总结。
|
||||
|
||||
## 1、numpy.random.rand(d0, d1, ..., dn)
|
||||
* 作用：产生一个给定形状的数组（其实应该是ndarray对象或者是一个单值），数组中的值服从[0, 1)之间的均匀分布。
|
||||
* 参数：d0, d, ..., dn : int，可选。如果没有参数则返回一个float型的随机数，该随机数服从[0, 1)之间的均匀分布。
|
||||
* 返回值：ndarray对象或者一个float型的值
|
||||
例子：
|
||||
```py
|
||||
# [0, 1)之间均匀分布的随机数，3行2列
|
||||
a = np.random.rand(3, 2)
|
||||
print(a)
|
||||
# 不提供形状
|
||||
b = np.random.rand()
|
||||
print(b)
|
||||
输出：
|
||||
|
||||
[[0.26054323 0.28184468]
|
||||
[0.7783674 0.71733674]
|
||||
[0.90302256 0.49303252]]
|
||||
0.6022098740124009
|
||||
```
|
||||
## 2、numpy.random.uniform(low=0.0, high=1.0, size=None)
|
||||
* 作用：返回一个在区间[low, high)中均匀分布的数组，size指定形状。
|
||||
* 参数：
|
||||
* low, high：float型或者float型的类数组对象。指定抽样区间为[low, high)，low的默认值为0.0，hign的默认值为1.0
|
||||
* size：int型或int型元组。指定形状，如果不提供size，则返回一个服从该分布的随机数。
|
||||
例子：
|
||||
```py
|
||||
# 在[1, 10)之间均匀抽样，数组形状为3行2列
|
||||
a = np.random.uniform(1, 10, (3, 2))
|
||||
print(a)
|
||||
# 不提供size
|
||||
b = np.random.uniform(1, 10)
|
||||
print(b)
|
||||
输出：
|
||||
|
||||
[[5.16545387 6.3769087 ]
|
||||
[9.98964899 7.88833885]
|
||||
[1.37173855 4.19855075]]
|
||||
3.899250175275188
|
||||
```
|
||||
## 3、numpy.random.randn(d0, d1, ..., dn)
|
||||
* 作用：返回一个指定形状的数组，数组中的值服从标准正态分布（均值为0，方差为1）。
|
||||
* 参数：d0, d, ..., dn : int，可选。如果没有参数，则返回一个服从标准正态分布的float型随机数。
|
||||
* 返回值：ndarray对象或者float
|
||||
例子：
|
||||
```py
|
||||
# 3行2列
|
||||
a = np.random.randn(3, 2)
|
||||
print(a)
|
||||
# 不提供形状
|
||||
b = np.random.randn()
|
||||
print(b)
|
||||
输出：
|
||||
|
||||
[[-1.46605527 0.35434705]
|
||||
[ 0.43408199 0.02689309]
|
||||
[ 0.48041554 1.62665755]]
|
||||
-0.6291254375915813
|
||||
```
|
||||
|
||||
## 4、numpy.random.normal(loc=0.0, scale=1.0, size=None)
|
||||
* 作用：返回一个由size指定形状的数组，数组中的值服从 μ=loc,σ=scale 的正态分布。
|
||||
* 参数：
|
||||
* loc : float型或者float型的类数组对象，指定均值 μ
|
||||
* scale : float型或者float型的类数组对象，指定标准差 σ
|
||||
* size : int型或者int型的元组，指定了数组的形状。如果不提供size，且loc和scale为标量（不是类数组对象），则返回一个服从该分布的随机数。
|
||||
* 输出：ndarray对象或者一个标量
|
||||
例子：
|
||||
```py
|
||||
# 标准正态分布，3行2列
|
||||
a = np.random.normal(0, 1, (3, 2))
|
||||
print(a)
|
||||
# 均值为1，标准差为3
|
||||
b = np.random.normal(1, 3)
|
||||
print(b)
|
||||
输出：
|
||||
|
||||
[[ 0.34912031 -0.08757564]
|
||||
[-0.99753101 0.37441719]
|
||||
[ 2.68072286 -1.03663963]]
|
||||
5.770831320998463
|
||||
```
|
||||
## 5、numpy.random.randint(low, high=None, size=None, dtype='l')
|
||||
* 作用：返回一个在区间[low, high)中离散均匀抽样的数组，size指定形状，dtype指定数据类型。
|
||||
* 参数：
|
||||
* low, high：int型，指定抽样区间[low, high)
|
||||
* size：int型或int型的元组，指定形状
|
||||
* dypte：可选参数，指定数据类型，比如int,int64等，默认是np.int
|
||||
* 返回值：如果指定了size，则返回一个int型的ndarray对象，否则返回一个服从该分布的int型随机数。
|
||||
例子：
|
||||
```py
|
||||
# 在[1, 10)之间离散均匀抽样，数组形状为3行2列
|
||||
a = np.random.randint(1, 10, (3, 2))
|
||||
print(a)
|
||||
# 不提供size
|
||||
b = np.random.randint(1, 10)
|
||||
print(b)
|
||||
# 指定dtype
|
||||
c = np.random.randint(1, 10, dtype=np.int64)
|
||||
print(c)
|
||||
type(c)
|
||||
输出：
|
||||
|
||||
[[3 1]
|
||||
[3 3]
|
||||
[5 8]]
|
||||
6
|
||||
2
|
||||
numpy.int64
|
||||
```
|
||||
|
||||
## 6、numpy.random.random(size=None)
|
||||
* 作用：返回从[0, 1)之间均匀抽样的数组，size指定形状。
|
||||
* 参数：
|
||||
* size：int型或int型的元组，如果不提供则返回一个服从该分布的随机数
|
||||
* 返回值：float型或者float型的ndarray对象
|
||||
* 例子：
|
||||
```py
|
||||
# [0, 1)之间的均匀抽样，3行2列
|
||||
a = np.random.random((3, 2))
|
||||
print(a)
|
||||
# 不指定size
|
||||
b = np.random.random()
|
||||
print(b)
|
||||
输出：
|
||||
|
||||
[[0.80136714 0.63129059]
|
||||
[0.04556679 0.04433006]
|
||||
[0.09643599 0.53312761]]
|
||||
0.32828505898057136
|
||||
```
|
||||
|
||||
# numpy API
|
||||
|
||||
## 简单的随机数据
|
||||
|
||||
<div id="cnblogs_post_body" class="blogpost-body">
|
||||
<h1>随机抽样 (<tt class="xref py py-mod docutils literal"><span class="pre">numpy.random</span></tt>)</h1>
|
||||
<div id="simple-random-data" class="section">
|
||||
<h2><a name="t1"></a>简单的随机数据</h2>
|
||||
<table class="longtable docutils" border="1"><colgroup><col width="40%"><col width="60%"></colgroup>
|
||||
<tbody valign="top">
|
||||
<tr class="row-odd">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.rand" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.rand.html#numpy.random.rand" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">rand</span></tt></a>(d0, d1, ..., dn)</p>
|
||||
</td>
|
||||
<td>
|
||||
<p>随机值</p>
|
||||
<div class="cnblogs_code">
|
||||
<pre>>>> np.random.rand(3,2)
|
||||
array([[ 0.14022471, 0.96360618], #random
|
||||
[ 0.37601032, 0.25528411], #random
|
||||
[ 0.49313049, 0.94909878]]) #random</pre>
|
||||
</div>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="row-even">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.randn" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.randn.html#numpy.random.randn" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">randn</span></tt></a>(d0, d1, ..., dn)</p>
|
||||
</td>
|
||||
<td>
|
||||
<p>返回一个样本，具有标准正态分布。</p>
|
||||
<p class="rubric">Notes</p>
|
||||
<p>For random samples from <img class="math" src="http://docs.scipy.org/doc/numpy/_images/math/93af1f49bf6bbf05f549f49609becdb5f7039538.png" alt="技术分享">, use:</p>
|
||||
<div class="cnblogs_code">
|
||||
<pre>sigma * np.random.randn(...) + mu</pre>
|
||||
</div>
|
||||
<p class="rubric">Examples</p>
|
||||
<div class="cnblogs_code">
|
||||
<pre>>>> np.random.randn()
|
||||
2.1923875335537315 #random</pre>
|
||||
</div>
|
||||
<p>Two-by-four array of samples from N(3, 6.25):</p>
|
||||
<div class="cnblogs_code">
|
||||
<pre>>>> 2.5 * np.random.randn(2, 4) + 3
|
||||
array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], #random
|
||||
[ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) #random</pre>
|
||||
</div>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="row-odd">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.randint" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.randint.html#numpy.random.randint" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">randint</span></tt></a>(low[, high, size])</p>
|
||||
</td>
|
||||
<td>
|
||||
<p>返回随机的整数，位于半开区间 [low, high)。</p>
|
||||
<div class="cnblogs_code">
|
||||
<pre>>>> np.random.randint(2, size=10)
|
||||
array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])
|
||||
>>> np.random.randint(1, size=10)
|
||||
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])</pre>
|
||||
</div>
|
||||
<p>Generate a 2 x 4 array of ints between 0 and 4, inclusive:</p>
|
||||
<div class="cnblogs_code">
|
||||
<pre>>>> np.random.randint(5, size=(2, 4))
|
||||
array([[4, 0, 2, 1],
|
||||
[3, 2, 2, 0]])</pre>
|
||||
</div>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="row-even">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.random_integers" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.random_integers.html#numpy.random.random_integers" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">random_integers</span></tt></a>(low[, high, size])</p>
|
||||
</td>
|
||||
<td>
|
||||
<p>返回随机的整数，位于闭区间 [low, high]。</p>
|
||||
<p class="rubric">Notes</p>
|
||||
<p>To sample from N evenly spaced floating-point numbers between a and b, use:</p>
|
||||
<div class="cnblogs_code">
|
||||
<pre>a + (b - a) * (np.random.random_integers(N) - 1) / (N - 1.)</pre>
|
||||
</div>
|
||||
<p>Examples</p>
|
||||
<div class="cnblogs_code"><div class="cnblogs_code_toolbar"><span class="cnblogs_code_copy"><a href="javascript:void(0);" onclick="copyCnblogsCode(this)" title="复制代码"><img src="//common.cnblogs.com/images/copycode.gif" alt="复制代码"></a></span></div>
|
||||
<pre>>>> np.random.random_integers(5)
|
||||
4
|
||||
>>> type(np.random.random_integers(5))
|
||||
<type ‘int‘>
|
||||
>>> np.random.random_integers(5, size=(3.,2.))
|
||||
array([[5, 4],
|
||||
[3, 3],
|
||||
[4, 5]])</pre>
|
||||
<div class="cnblogs_code_toolbar"><span class="cnblogs_code_copy"><a href="javascript:void(0);" onclick="copyCnblogsCode(this)" title="复制代码"><img src="//common.cnblogs.com/images/copycode.gif" alt="复制代码"></a></span></div></div>
|
||||
<p>Choose five random numbers from the set of five evenly-spaced numbers between 0 and 2.5, inclusive (<em>i.e.</em>, from the set <img class="math" src="http://docs.scipy.org/doc/numpy/_images/math/260812782a8a4f35a929d637a38520175045eaa2.png" alt="技术分享">):</p>
|
||||
<div class="cnblogs_code">
|
||||
<pre>>>> 2.5 * (np.random.random_integers(5, size=(5,)) - 1) / 4.
|
||||
array([ 0.625, 1.25 , 0.625, 0.625, 2.5 ])</pre>
|
||||
</div>
|
||||
<p>Roll two six sided dice 1000 times and sum the results:</p>
|
||||
<div class="cnblogs_code">
|
||||
<pre>>>> d1 = np.random.random_integers(1, 6, 1000)
|
||||
>>> d2 = np.random.random_integers(1, 6, 1000)
|
||||
>>> dsums = d1 + d2</pre>
|
||||
</div>
|
||||
<p>Display results as a histogram:</p>
|
||||
<div class="cnblogs_code">
|
||||
<pre>>>> import matplotlib.pyplot as plt
|
||||
>>> count, bins, ignored = plt.hist(dsums, 11, normed=True)
|
||||
>>> plt.show()</pre>
|
||||
</div>
|
||||
<p> </p>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="row-odd">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.random_sample" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.random_sample.html#numpy.random.random_sample" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">random_sample</span></tt></a>([size])</p>
|
||||
</td>
|
||||
<td>
|
||||
<p>返回随机的浮点数，在半开区间 [0.0, 1.0)。</p>
|
||||
<p>To sample <img class="math" src="http://docs.scipy.org/doc/numpy/_images/math/e9a05a99f961e8f094b3869fcddd366857d7b0d9.png" alt="技术分享"> multiply the output of <a class="reference internal" title="numpy.random.random_sample" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.random_sample.html#numpy.random.random_sample" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">random_sample</span></tt></a> by <em class="xref py py-obj">(b-a)</em> and add <em class="xref py py-obj">a</em>:</p>
|
||||
<div class="cnblogs_code">
|
||||
<pre>(b - a) * random_sample() + a</pre>
|
||||
</div>
|
||||
<p>Examples</p>
|
||||
<div class="cnblogs_code">
|
||||
<pre>>>> np.random.random_sample()
|
||||
0.47108547995356098
|
||||
>>> type(np.random.random_sample())
|
||||
<type ‘float‘>
|
||||
>>> np.random.random_sample((5,))
|
||||
array([ 0.30220482, 0.86820401, 0.1654503 , 0.11659149, 0.54323428])</pre>
|
||||
</div>
|
||||
<p>Three-by-two array of random numbers from [-5, 0):</p>
|
||||
<div class="cnblogs_code">
|
||||
<pre>>>> 5 * np.random.random_sample((3, 2)) - 5
|
||||
array([[-3.99149989, -0.52338984],
|
||||
[-2.99091858, -0.79479508],
|
||||
[-1.23204345, -1.75224494]])</pre>
|
||||
</div>
|
||||
<p> </p>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="row-even">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.random" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.random.html#numpy.random.random" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">random</span></tt></a>([size])</p>
|
||||
</td>
|
||||
<td>
|
||||
<p>返回随机的浮点数，在半开区间 [0.0, 1.0)。</p>
|
||||
<p>（官网例子与random_sample完全一样）</p>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="row-odd">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.ranf" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.ranf.html#numpy.random.ranf" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">ranf</span></tt></a>([size])</p>
|
||||
</td>
|
||||
<td>
|
||||
<p>返回随机的浮点数，在半开区间 [0.0, 1.0)。</p>
|
||||
<p>（官网例子与random_sample完全一样）</p>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="row-even">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.sample" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.sample.html#numpy.random.sample" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">sample</span></tt></a>([size])</p>
|
||||
</td>
|
||||
<td>
|
||||
<p>返回随机的浮点数，在半开区间 [0.0, 1.0)。</p>
|
||||
<p>（官网例子与random_sample完全一样）</p>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="row-odd">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.choice" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.choice.html#numpy.random.choice" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">choice</span></tt></a>(a[, size, replace, p])</p>
|
||||
</td>
|
||||
<td>
|
||||
<p>生成一个随机样本，从一个给定的一维数组</p>
|
||||
<p class="rubric">Examples</p>
|
||||
<p>Generate a uniform random sample from np.arange(5) of size 3:</p>
|
||||
<div class="cnblogs_code">
|
||||
<pre>>>> np.random.choice(5, 3)
|
||||
array([0, 3, 4])
|
||||
>>> #This is equivalent to np.random.randint(0,5,3)</pre>
|
||||
</div>
|
||||
<p>Generate a non-uniform random sample from np.arange(5) of size 3:</p>
|
||||
<div class="cnblogs_code">
|
||||
<pre>>>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])
|
||||
array([3, 3, 0])</pre>
|
||||
</div>
|
||||
<p>Generate a uniform random sample from np.arange(5) of size 3 without replacement:</p>
|
||||
<div class="cnblogs_code">
|
||||
<pre>>>> np.random.choice(5, 3, replace=False)
|
||||
array([3,1,0])
|
||||
>>> #This is equivalent to np.random.permutation(np.arange(5))[:3]</pre>
|
||||
</div>
|
||||
<p>Generate a non-uniform random sample from np.arange(5) of size 3 without replacement:</p>
|
||||
<div class="cnblogs_code">
|
||||
<pre>>>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])
|
||||
array([2, 3, 0])</pre>
|
||||
</div>
|
||||
<p>Any of the above can be repeated with an arbitrary array-like instead of just integers. For instance:</p>
|
||||
<div class="cnblogs_code">
|
||||
<pre>>>> aa_milne_arr = [‘pooh‘, ‘rabbit‘, ‘piglet‘, ‘Christopher‘]
|
||||
>>> np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])
|
||||
array([‘pooh‘, ‘pooh‘, ‘pooh‘, ‘Christopher‘, ‘piglet‘],
|
||||
dtype=‘|S11‘)</pre>
|
||||
</div>
|
||||
<p> </p>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="row-even">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.bytes" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.bytes.html#numpy.random.bytes" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">bytes</span></tt></a>(length)</p>
|
||||
</td>
|
||||
<td>
|
||||
<p>返回随机字节。</p>
|
||||
<div class="cnblogs_code">
|
||||
<pre>>>> np.random.bytes(10)
|
||||
‘ eh\x85\x022SZ\xbf\xa4‘ #random</pre>
|
||||
</div>
|
||||
<p> </p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
<div id="permutations" class="section">
|
||||
<h2><a name="t2"></a>排列</h2>
|
||||
<table class="longtable docutils" border="1"><colgroup><col width="40%"><col width="60%"></colgroup>
|
||||
<tbody valign="top">
|
||||
<tr class="row-odd">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.shuffle" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.shuffle.html#numpy.random.shuffle" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">shuffle</span></tt></a>(x)</p>
|
||||
</td>
|
||||
<td>
|
||||
<p>现场修改序列，改变自身内容。（类似洗牌，打乱顺序）</p>
|
||||
<div class="cnblogs_code">
|
||||
<pre>>>> arr = np.arange(10)
|
||||
>>> np.random.shuffle(arr)
|
||||
>>> arr
|
||||
[1 7 5 2 9 4 3 6 0 8]</pre>
|
||||
</div>
|
||||
<p> </p>
|
||||
<p>This function only shuffles the array along the first index of a multi-dimensional array:</p>
|
||||
<div class="cnblogs_code">
|
||||
<pre>>>> arr = np.arange(9).reshape((3, 3))
|
||||
>>> np.random.shuffle(arr)
|
||||
>>> arr
|
||||
array([[3, 4, 5],
|
||||
[6, 7, 8],
|
||||
[0, 1, 2]])</pre>
|
||||
</div>
|
||||
<p> </p>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="row-even">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.permutation" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.permutation.html#numpy.random.permutation" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">permutation</span></tt></a>(x)</p>
|
||||
</td>
|
||||
<td>
|
||||
<p>返回一个随机排列</p>
|
||||
<div class="cnblogs_code">
|
||||
<pre>>>> np.random.permutation(10)
|
||||
array([1, 7, 4, 3, 0, 9, 2, 5, 8, 6])</pre>
|
||||
</div>
|
||||
<div class="cnblogs_code">
|
||||
<pre>>>> np.random.permutation([1, 4, 9, 12, 15])
|
||||
array([15, 1, 9, 4, 12])</pre>
|
||||
</div>
|
||||
<div class="cnblogs_code">
|
||||
<pre>>>> arr = np.arange(9).reshape((3, 3))
|
||||
>>> np.random.permutation(arr)
|
||||
array([[6, 7, 8],
|
||||
[0, 1, 2],
|
||||
[3, 4, 5]])</pre>
|
||||
</div>
|
||||
<p> </p>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
<div id="distributions" class="section">
|
||||
<h2><a name="t3"></a><span class="web-item">分布</span></h2>
|
||||
<table class="longtable docutils" border="1"><colgroup><col width="40%"><col width="60%"></colgroup>
|
||||
<tbody valign="top">
|
||||
<tr class="row-odd">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.beta" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.beta.html#numpy.random.beta" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">beta</span></tt></a>(a, b[, size])</p>
|
||||
</td>
|
||||
<td>贝塔分布样本，在 <tt class="docutils literal"><span class="pre">[0, <span class="pre">1]</span></span></tt>内。</td>
|
||||
</tr>
|
||||
<tr class="row-even">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.binomial" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.binomial.html#numpy.random.binomial" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">binomial</span></tt></a>(n, p[, size])</p>
|
||||
</td>
|
||||
<td>二项分布的样本。</td>
|
||||
</tr>
|
||||
<tr class="row-odd">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.chisquare" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.chisquare.html#numpy.random.chisquare" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">chisquare</span></tt></a>(df[, size])</p>
|
||||
</td>
|
||||
<td>卡方分布样本。</td>
|
||||
</tr>
|
||||
<tr class="row-even">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.dirichlet" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.dirichlet.html#numpy.random.dirichlet" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">dirichlet</span></tt></a>(alpha[, size])</p>
|
||||
</td>
|
||||
<td>狄利克雷分布样本。</td>
|
||||
</tr>
|
||||
<tr class="row-odd">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.exponential" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.exponential.html#numpy.random.exponential" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">exponential</span></tt></a>([scale, size])</p>
|
||||
</td>
|
||||
<td>指数分布</td>
|
||||
</tr>
|
||||
<tr class="row-even">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.f" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.f.html#numpy.random.f" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">f</span></tt></a>(dfnum, dfden[, size])</p>
|
||||
</td>
|
||||
<td>F分布样本。</td>
|
||||
</tr>
|
||||
<tr class="row-odd">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.gamma" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.gamma.html#numpy.random.gamma" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">gamma</span></tt></a>(shape[, scale, size])</p>
|
||||
</td>
|
||||
<td>伽马分布</td>
|
||||
</tr>
|
||||
<tr class="row-even">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.geometric" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.geometric.html#numpy.random.geometric" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">geometric</span></tt></a>(p[, size])</p>
|
||||
</td>
|
||||
<td>几何分布</td>
|
||||
</tr>
|
||||
<tr class="row-odd">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.gumbel" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.gumbel.html#numpy.random.gumbel" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">gumbel</span></tt></a>([loc, scale, size])</p>
|
||||
</td>
|
||||
<td>耿贝尔分布。</td>
|
||||
</tr>
|
||||
<tr class="row-even">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.hypergeometric" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.hypergeometric.html#numpy.random.hypergeometric" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">hypergeometric</span></tt></a>(ngood, nbad, nsample[, size])</p>
|
||||
</td>
|
||||
<td>超几何分布样本。</td>
|
||||
</tr>
|
||||
<tr class="row-odd">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.laplace" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.laplace.html#numpy.random.laplace" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">laplace</span></tt></a>([loc, scale, size])</p>
|
||||
</td>
|
||||
<td>拉普拉斯或双指数分布样本</td>
|
||||
</tr>
|
||||
<tr class="row-even">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.logistic" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.logistic.html#numpy.random.logistic" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">logistic</span></tt></a>([loc, scale, size])</p>
|
||||
</td>
|
||||
<td>Logistic分布样本</td>
|
||||
</tr>
|
||||
<tr class="row-odd">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.lognormal" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.lognormal.html#numpy.random.lognormal" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">lognormal</span></tt></a>([mean, sigma, size])</p>
|
||||
</td>
|
||||
<td>对数正态分布</td>
|
||||
</tr>
|
||||
<tr class="row-even">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.logseries" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.logseries.html#numpy.random.logseries" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">logseries</span></tt></a>(p[, size])</p>
|
||||
</td>
|
||||
<td>对数级数分布。</td>
|
||||
</tr>
|
||||
<tr class="row-odd">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.multinomial" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.multinomial.html#numpy.random.multinomial" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">multinomial</span></tt></a>(n, pvals[, size])</p>
|
||||
</td>
|
||||
<td>多项分布</td>
|
||||
</tr>
|
||||
<tr class="row-even">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.multivariate_normal" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.multivariate_normal.html#numpy.random.multivariate_normal" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">multivariate_normal</span></tt></a>(mean, cov[, size])</p>
|
||||
</td>
|
||||
<td>
|
||||
<p>多元正态分布。</p>
|
||||
<div class="cnblogs_code">
|
||||
<pre>>>> mean = [0,0]
|
||||
>>> cov = [[1,0],[0,100]] # diagonal covariance, points lie on x or y-axis</pre>
|
||||
</div>
|
||||
<div class="cnblogs_code">
|
||||
<pre>>>> import matplotlib.pyplot as plt
|
||||
>>> x, y = np.random.multivariate_normal(mean, cov, 5000).T
|
||||
>>> plt.plot(x, y, ‘x‘); plt.axis(‘equal‘); plt.show()</pre>
|
||||
</div>
|
||||
<p> </p>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="row-odd">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.negative_binomial" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.negative_binomial.html#numpy.random.negative_binomial" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">negative_binomial</span></tt></a>(n, p[, size])</p>
|
||||
</td>
|
||||
<td>负二项分布</td>
|
||||
</tr>
|
||||
<tr class="row-even">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.noncentral_chisquare" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.noncentral_chisquare.html#numpy.random.noncentral_chisquare" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">noncentral_chisquare</span></tt></a>(df, nonc[, size])</p>
|
||||
</td>
|
||||
<td>非中心卡方分布</td>
|
||||
</tr>
|
||||
<tr class="row-odd">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.noncentral_f" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.noncentral_f.html#numpy.random.noncentral_f" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">noncentral_f</span></tt></a>(dfnum, dfden, nonc[, size])</p>
|
||||
</td>
|
||||
<td>非中心F分布</td>
|
||||
</tr>
|
||||
<tr class="row-even">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.normal" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.normal.html#numpy.random.normal" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">normal</span></tt></a>([loc, scale, size])</p>
|
||||
</td>
|
||||
<td>
|
||||
<p>正态(高斯)分布</p>
|
||||
<p class="rubric">Notes</p>
|
||||
<p>The probability density for the Gaussian distribution is</p>
|
||||
<div class="math">
|
||||
<p><img src="http://docs.scipy.org/doc/numpy/_images/math/2f8a02ce155191ed5a4ea8d776aa15fcaef26e1f.png" alt="技术分享"></p>
|
||||
</div>
|
||||
<p>where <img class="math" src="http://docs.scipy.org/doc/numpy/_images/math/fb6d665bbe0c01fc1af5c5f5fa7df40dc71331d7.png" alt="技术分享"> is the mean and <img class="math" src="http://docs.scipy.org/doc/numpy/_images/math/bb6e1902efeb0b3704c6191ddce1f02707ab7d4b.png" alt="技术分享"> the standard deviation. The square of the standard deviation, <img class="math" src="http://docs.scipy.org/doc/numpy/_images/math/dd3f23ceebfef553bff1607f84667d5cc6af7587.png" alt="技术分享">, is called the variance.</p>
|
||||
<p>The function has its peak at the mean, and its “spread” increases with the standard deviation (the function reaches 0.607 times its maximum at <img class="math" src="http://docs.scipy.org/doc/numpy/_images/math/ad6d5400fce43a299a35b53a9661e54a284f1bad.png" alt="技术分享"> and <img class="math" src="http://docs.scipy.org/doc/numpy/_images/math/fa3af8846dae3eb1c8913dace238ff110abb64b2.png" alt="技术分享"><a id="id3" class="reference internal" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.normal.html#r217" target="_blank"> [R217]</a>).</p>
|
||||
<p> </p>
|
||||
<p class="rubric">Examples</p>
|
||||
<p>Draw samples from the distribution:</p>
|
||||
<div class="cnblogs_code">
|
||||
<pre>>>> mu, sigma = 0, 0.1 # mean and standard deviation
|
||||
>>> s = np.random.normal(mu, sigma, 1000)</pre>
|
||||
</div>
|
||||
<p>Verify the mean and the variance:</p>
|
||||
<div class="cnblogs_code">
|
||||
<pre>>>> abs(mu - np.mean(s)) < 0.01
|
||||
True
|
||||
>>> abs(sigma - np.std(s, ddof=1)) < 0.01
|
||||
True</pre>
|
||||
</div>
|
||||
<p>Display the histogram of the samples, along with the probability density function:</p>
|
||||
<div class="cnblogs_code">
|
||||
<pre>>>> import matplotlib.pyplot as plt
|
||||
>>> count, bins, ignored = plt.hist(s, 30, normed=True)
|
||||
>>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *
|
||||
... np.exp( - (bins - mu)**2 / (2 * sigma**2) ),
|
||||
... linewidth=2, color=‘r‘)
|
||||
>>> plt.show()</pre>
|
||||
</div>
|
||||
<p> </p>
|
||||
</td>
|
||||
</tr>
|
||||
<tr class="row-odd">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.pareto" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.pareto.html#numpy.random.pareto" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">pareto</span></tt></a>(a[, size])</p>
|
||||
</td>
|
||||
<td>帕累托（Lomax）分布</td>
|
||||
</tr>
|
||||
<tr class="row-even">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.poisson" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.poisson.html#numpy.random.poisson" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">poisson</span></tt></a>([lam, size])</p>
|
||||
</td>
|
||||
<td>泊松分布</td>
|
||||
</tr>
|
||||
<tr class="row-odd">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.power" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.power.html#numpy.random.power" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">power</span></tt></a>(a[, size])</p>
|
||||
</td>
|
||||
<td>Draws samples in [0, 1] from a power distribution with positive exponent a - 1.</td>
|
||||
</tr>
|
||||
<tr class="row-even">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.rayleigh" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.rayleigh.html#numpy.random.rayleigh" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">rayleigh</span></tt></a>([scale, size])</p>
|
||||
</td>
|
||||
<td>Rayleigh 分布</td>
|
||||
</tr>
|
||||
<tr class="row-odd">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.standard_cauchy" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.standard_cauchy.html#numpy.random.standard_cauchy" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">standard_cauchy</span></tt></a>([size])</p>
|
||||
</td>
|
||||
<td>标准柯西分布</td>
|
||||
</tr>
|
||||
<tr class="row-even">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.standard_exponential" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.standard_exponential.html#numpy.random.standard_exponential" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">standard_exponential</span></tt></a>([size])</p>
|
||||
</td>
|
||||
<td>标准的指数分布</td>
|
||||
</tr>
|
||||
<tr class="row-odd">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.standard_gamma" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.standard_gamma.html#numpy.random.standard_gamma" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">standard_gamma</span></tt></a>(shape[, size])</p>
|
||||
</td>
|
||||
<td>标准伽马分布</td>
|
||||
</tr>
|
||||
<tr class="row-even">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.standard_normal" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.standard_normal.html#numpy.random.standard_normal" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">standard_normal</span></tt></a>([size])</p>
|
||||
</td>
|
||||
<td>标准正态分布 (mean=0, stdev=1).</td>
|
||||
</tr>
|
||||
<tr class="row-odd">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.standard_t" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.standard_t.html#numpy.random.standard_t" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">standard_t</span></tt></a>(df[, size])</p>
|
||||
</td>
|
||||
<td>Standard Student’s t distribution with df degrees of freedom.</td>
|
||||
</tr>
|
||||
<tr class="row-even">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.triangular" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.triangular.html#numpy.random.triangular" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">triangular</span></tt></a>(left, mode, right[, size])</p>
|
||||
</td>
|
||||
<td>三角形分布</td>
|
||||
</tr>
|
||||
<tr class="row-odd">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.uniform" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.uniform.html#numpy.random.uniform" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">uniform</span></tt></a>([low, high, size])</p>
|
||||
</td>
|
||||
<td>均匀分布</td>
|
||||
</tr>
|
||||
<tr class="row-even">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.vonmises" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.vonmises.html#numpy.random.vonmises" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">vonmises</span></tt></a>(mu, kappa[, size])</p>
|
||||
</td>
|
||||
<td>von Mises分布</td>
|
||||
</tr>
|
||||
<tr class="row-odd">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.wald" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.wald.html#numpy.random.wald" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">wald</span></tt></a>(mean, scale[, size])</p>
|
||||
</td>
|
||||
<td>瓦尔德（逆高斯）分布</td>
|
||||
</tr>
|
||||
<tr class="row-even">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.weibull" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.weibull.html#numpy.random.weibull" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">weibull</span></tt></a>(a[, size])</p>
|
||||
</td>
|
||||
<td>Weibull 分布</td>
|
||||
</tr>
|
||||
<tr class="row-odd">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.zipf" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.zipf.html#numpy.random.zipf" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">zipf</span></tt></a>(a[, size])</p>
|
||||
</td>
|
||||
<td>齐普夫分布</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
<h2><a name="t4"></a>随机数生成器</h2>
|
||||
<table class="longtable docutils" border="1"><colgroup><col width="40%"><col width="60%"></colgroup>
|
||||
<tbody valign="top">
|
||||
<tr class="row-odd">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.RandomState" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.RandomState.html#numpy.random.RandomState" target="_blank"><tt class="xref py py-obj docutils literal">RandomState</tt></a></p>
|
||||
</td>
|
||||
<td>Container for the Mersenne Twister pseudo-random number generator.</td>
|
||||
</tr>
|
||||
<tr class="row-even">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.seed" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.seed.html#numpy.random.seed" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">seed</span></tt></a>([seed])</p>
|
||||
</td>
|
||||
<td>Seed the generator.</td>
|
||||
</tr>
|
||||
<tr class="row-odd">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.get_state" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.get_state.html#numpy.random.get_state" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">get_state</span></tt></a>()</p>
|
||||
</td>
|
||||
<td>Return a tuple representing the internal state of the generator.</td>
|
||||
</tr>
|
||||
<tr class="row-even">
|
||||
<td>
|
||||
<p><a class="reference internal" title="numpy.random.set_state" href="http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.set_state.html#numpy.random.set_state" target="_blank"><tt class="xref py py-obj docutils literal"><span class="pre">set_state</span></tt></a>(state)</p>
|
||||
</td>
|
||||
<td>Set the internal state of the generator from a tuple.</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
Reference in New Issue
Block a user