xinfeng2005的专栏

github:https://github.com/hmmlearn/hmmlearn使用说明：http://hmmlearn.readthedocs.io/en/latest/

#coding=utf-8import numpy as npfrom numpy.random import RandomStateimport tensorflow as tfw1=tf.Variable(tf.random_normal([2,3],stddev=1,seed=1))b1=tf.Variable(tf.constant(0.1, shape=[2]))w2=t

线性回归：1.目标函数增加L2正则θ存在解析式：使用梯度下降进行解θ：=>代码：import numpy as npdef regression(data, alpha, lamda): n=len(data[0])-1 theta=np.zeros(n) for i in range(30): for d in data:

SVM回归代码：import numpy as npfrom sklearn import svmimport matplotlib.pyplot as pltN = 50np.random.seed(0)x = np.sort(np.random.uniform(0, 6, N), axis=0)y = 2*np.sin(x) + 0.1*np.random

#coding=utf-8#########################################压缩from skimage import iofrom sklearn.cluster import KMeansimport numpy as npimage = io.imread('test.png')rows = image.shape[0]cols = im

import reimport maths=['this is yes','this is no']r=[0,1]def tokenize(message): message=message.lower() all_words = re.findall('[a-z0-9]+', message) print(all_words) return set(a