yyq675886993的博客

from gensim import corpora,models,similarities import jieba from collections import defaultdict import urllib.request#d1=open("C:/Users/yyq/Desktop/毕业论文/文档1.txt").read() #d2=open("C:/Users/yyq/Desktop/

from numpy import * import matplotlib.pyplot as pltdist = mat([[0.1,0.1],[0.9,0.5],[0.9,0.1],[0.45,0.9],[0.9,0.8],[0.7,0.9],[0.1,0.45],[0.45,0.1]]) m,n = shape(dist) fig = plt.figure() # 绘图 ax = fig.ad

#Numpy 提供了专门的矩阵数据结构和线性代数库，完全实现了矢量化编程 import numpy as np from numpy import * mylist = [1,2,3,4,5] a = 10 mymatrix = np.mat(mylist) print (a*mymatrix)#numpy 矩阵运算 myZero = np.zeros([3,5]) # 3*5 的全零矩阵 prin

import numpy as np from numpy import * import matplotlib.pyplot as plt # 测试数据集-二维 list dataSet = [[-0.017612,14.053064],[-1.395634 ,4.662541],[-0.752157 ,6.538620],[-1.322371 ,7.152853],[0.423363 ,11

import numpy from os import listdir import jieba import operator from gensim import corpora,models,similarities from numpy import *#贝叶斯算法的实现 class Bayes: def __init__(self): self.length=-1

#1.数据准备 import pandas as pda import numpy as npy filename="" dataf=pda.read_csv(filename) x=dataf.iloc[:,1:4].as_matrix() y=dataf.iloc[:,0:1].as_matrix()#2.数据的归一化 from sklearn import preprocessing #归一化

import jieba from collections import defaultdict import os from sklearn.feature_extraction.text import TfidfTransformer from sklearn.feature_extraction.text import CountVectorizer def readfile(filename