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Thought is already is late, exactly is the earliest time.
这周老师又出差，那个文件的东西没有搞，不过也应该很好弄。。这个周前几天，老师一直跟我讨论脚步检测的可行性问题，今天上午做了PPT，等晚上和师兄师姐们讨论一下。看一下这个工作的可行性究竟有多大！
然后，这个周的晚上都有点小堕落。以后要抓紧时间学习啦！
然后是课程方面的东西，还有两个周就要相继考试了，总共有3们课程！恩。。要抓紧时间啦！矩阵论+机器学习+图像分析与计算机视觉！这些东西还是蛮多的！
然后这个周，就是把机器学习，算法，计算机视觉的那个给搞定！。。。
看了很多东西，昨天晚上看了一个台湾交通大学的一个视频，恩，挺不错的！学到了很多东西。。我还需要继续学习！
然后是实验室的各种事情，静下来好好做点东西。。。
x2fx -Convert predictor matrix to design matrix
Syntax
D = x2fx(X,model)
D = x2fx(X,model,categ)
D = x2fx(X,model,categ,catlevels)
Description
D = x2fx(X,model) converts a matrix of predictors X to a design matrix D for regression analysis. Distinct predictor variables should appear in different columns of X.
The optional input model controls the regression model. By default, x2fx returns the design matrix for a linear additive model with a constant term. model is one of the following strings:
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'linear' — Constant and linear terms. This is the default.
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'interaction' — Constant, linear, and interaction terms
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'quadratic' — Constant, linear, interaction, and squared terms
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'purequadratic' — Constant, linear, and squared terms
If X has n columns, the order of the columns of D for a full quadratic model is:
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The constant term
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The linear terms (the columns of X, in order 1, 2, ..., n)
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The interaction terms (pairwise products of the columns of X, in order (1, 2), (1, 3), ..., (1, n), (2, 3), ..., (n–1, n))
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The squared terms (in order 1, 2, ..., n)
Other models use a subset of these terms, in the same order.
这个还是还是挺方便的。。