http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.normal.html #np.random.normal,产生制定分布的数集#http://docs.scipy.org/doc/numpy/reference/generated/numpy.random.normal.html# mean and standard deviation# 均值的物理意义mu,Mean (“centre”) of the distr
#网址 http://docs.scipy.org/doc/numpy/reference/generated/numpy.matlib.randn.html#numpy.matlib.randn numpy.matlib.randn numpy.matlib.randn(*args)[source] Return a random matrix with data from the “standard normal” distribution. randn generates a matrix
chapter 3: Linear Methods for Regression 第3章:回归的线性方法 3.1 Introduction A linear regression model assumes that the regression function \(E(Y\mid X)\) is linear in the inputs \(X_1, \ldots , X_p\). Linear models were largely developed in the precomputer
近期看了一篇文章<spatiograms versus histograms for region-based tracking>,在此把这篇文章的核心思想及算法推理进行整理. 空间直方图 传统直方图可视为零阶空间直方图,二阶空间直 方图包含直方图每一个bin的空间均值和协方差.这样的空间信息能获取目标更丰富的特征描写叙述.从而提高了跟踪的鲁棒性. watermark/2/text/aHR0cDovL2Jsb2cuY3Nkbi5uZXQv/font/5a6L5L2T/fontsize/400/
① numpy中np.c_和np.r_ np.r_是按列连接两个矩阵,就是把两矩阵上下相加,要求列数相等,类似于pandas中的concat(). np.c_是按行连接两个矩阵,就是把两矩阵左右相加,要求行数相等,类似于pandas中的merge(). 下面看一个例子: import numpy as np a = np.array([1, 2, 3]) b = np.array([4, 5, 6]) c = np.c_[a,b] print(np.r_[a,b]) print(c) print