所以我需要计算N个变量的联合概率分布.我有两个变量的代码,但我很难将它推广到更高的维度.我想有一些pythonic矢量化可能会有所帮助,但是,现在我的代码非常像C(是的,我知道这不是编写Python的正确方法).我的2D代码如下:
import numpy import math feature1 = numpy.array([1.1,2.2,3.0,1.2,5.4,3.4,2.2,6.8,4.5,5.6,1.9,2.8,3.7,4.4,7.3,8.3,8.1,7.0,8.0,6.8,6.2,4.9,5.7,6.3,3.7,2.4,4.5,8.5,9.5,9.9]); feature2 = numpy.array([11.1,12.8,13.0,11.6,15.2,13.8,11.1,17.8,12.5,15.2,11.6,20.8,14.7,14.4,15.3,18.3,11.4,17.0,16.0,16.8,12.2,14.9,15.7,16.3,13.7,12.4,14.2,18.5,19.8,19.0]); #===Concatenate All Features===# numFrames = len(feature1); allFeatures = numpy.zeros((2,numFrames)); allFeatures[0,:] = feature1; allFeatures[1,:] = feature2; #===Create the Array to hold all the Bins===# numBins = int(0.25*numFrames); allBins = numpy.zeros((allFeatures.shape[0],numBins+1)); #===Find the maximum and minimum of each feature===# allRanges = numpy.zeros((allFeatures.shape[0],2)); for f in range(allFeatures.shape[0]): allRanges[f,0] = numpy.amin(allFeatures[f,:]); allRanges[f,1] = numpy.amax(allFeatures[f,:]); #===Create the Array to hold all the individual feature probabilities===# allIndividualProbs = numpy.zeros((allFeatures.shape[0],numBins)); #===Grab all the Individual Probs and the Bins===# for f in range(allFeatures.shape[0]): freqhist, binedges = numpy.histogram(allFeatures[f,:],bins=numBins,range=[allRanges[f,0],allRanges[f,1]],density=False); allBins[f,:] = binedges; allIndividualProbs[f,:] = freqhist; #===Create the joint probability array===# jointProbs = numpy.zeros((numBins,numBins)); #===Compute the joint probability distribution===# numElements = 0; for b1 in range(numBins): for b2 in range(numBins): for f1 in range(numFrames): for f2 in range(numFrames): if ( ( (feature1[f1] >= allBins[0,b1]) and (feature1[f1] <= allBins[0,b1+1]) ) and ((feature2[f2] >= allBins[1,b2]) and (feature2[f2] <= allBins[1,b2+1])) ): jointProbs[b1,b2] += 1; numElements += 1; jointProbs /= numElements; #===But what if I add the following===# feature3 = numpy.array([21.1,21.8,23.5,27.6,25.2,23.8,22.1,22.8,26.5,25.2,28.6,20.8,24.7,24.4,29.3,28.3,27.4,26.0,26.2,26.1,25.9,24.0,22.7,22.3,23.7,26.4,24.2,28.5,29.8,29.0]);
如何推广大循环?对于N个变量(特征),这个循环将是巨大的.是否有Pythonic方法可以轻松完成此操作?
检查功能numpy.histogramdd
.此函数可以计算任意维数的直方图.如果设置参数normed=True
,则返回bin计数除以bin hypervolume.如果您更喜欢某种更像概率质量函数(其中所有内容总和为1)的东西,那么请自行规范化.总之,你会有类似的东西:
import numpy as np numBins = 10 # number of bins in each dimension data = np.random.randn(100000, 3) # generate 100000 3-d random data points jointProbs, edges = np.histogramdd(data, bins=numBins) jointProbs /= jointProbs.sum()