tf用到的matplotlib的helloworld

https://github.com/killinux/tensorflowbook
学习《tensorflow for machine intelligence》

在mac下使用matplotlib 可能会遇到问题
在环境上安装matplotlib
画图参考
https://blog.csdn.net/qq_34337272/article/details/79555544


conda install -n tensorenv matplotlib

参考
https://jingyan.baidu.com/article/eb9f7b6d4a1365869364e839.html

安装 matplotlib 之后，会在家目录生成一个 .matplotlib 文件夹。在这个文件夹中新建一个文件：matplotlibrc，内容是：backend: TkAgg。保存重新进入测试，问题解决

helloworld

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

a = tf.random_normal([2,20])
sess = tf.Session()
out = sess.run(a)
x, y = out

plt.scatter(x, y)
plt.show()

得到





kmeas实现：

conda create -n kmdemo python=3
source activate kmdemo
conda install -n kmdemo matplotlib numpy

输入文件

test.txt

1.65	4.28
-3.45	3.42
4.84	-1.15
-5.37	-3.36
0.97	2.92
-3.57	1.53
0.45	-3.30
-3.49	-1.72
2.67	1.59
-3.16	3.19

kmeans代码：

import numpy as np
import matplotlib.pyplot as plt

def loadDataSet(fileName):
    data = np.loadtxt(fileName,delimiter='\t')
    return data

def distEclud(x,y):
    return np.sqrt(np.sum((x-y)**2))

def randCent(dataSet,k):
    m,n = dataSet.shape
    centroids = np.zeros((k,n))
    for i in range(k):
        index = int(np.random.uniform(0,m)) #
        centroids[i,:] = dataSet[index,:]
    return centroids

def KMeans(dataSet,k):

    m = np.shape(dataSet)[0]
    clusterAssment = np.mat(np.zeros((m,2)))
    clusterChange = True

    centroids = randCent(dataSet,k)
    while clusterChange:
        clusterChange = False

        for i in range(m):
            minDist = 100000.0
            minIndex = -1

            for j in range(k):
                distance = distEclud(centroids[j,:],dataSet[i,:])
                if distance < minDist:
                    minDist = distance
                    minIndex = j
            if clusterAssment[i,0] != minIndex:
                clusterChange = True
                clusterAssment[i,:] = minIndex,minDist**2
        for j in range(k):
            pointsInCluster = dataSet[np.nonzero(clusterAssment[:,0].A == j)[0]]
            centroids[j,:] = np.mean(pointsInCluster,axis=0)

    print("Congratulations,cluster complete!")
    return centroids,clusterAssment

def showCluster(dataSet,k,centroids,clusterAssment):
    m,n = dataSet.shape
    if n != 2:
        print("data not two wei ")
        return 1

    mark = ['or', 'ob', 'og', 'ok', '^r', '+r', 'sr', 'dr', '<r', 'pr']
    if k > len(mark):
        print("k value too large")
        return 1

    for i in range(m):
        markIndex = int(clusterAssment[i,0])
        plt.plot(dataSet[i,0],dataSet[i,1],mark[markIndex])

    mark = ['Dr', 'Db', 'Dg', 'Dk', '^b', '+b', 'sb', 'db', '<b', 'pb']
    for i in range(k):
        plt.plot(centroids[i,0],centroids[i,1],mark[i])

    plt.show()
dataSet = loadDataSet("test.txt")
k = 4
centroids,clusterAssment = KMeans(dataSet,k)

showCluster(dataSet,k,centroids,clusterAssment)