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Thought is already is late, exactly is the earliest time.
這周老師又出差，那個文件的東西沒有搞，不過也應該很好弄。。這個周前幾天，老師一直跟我討論腳步檢測的可行性問題，今天上午做了PPT，等晚上和師兄師姐們討論一下?？匆幌逻@個工作的可行性究竟有多大！
然后，這個周的晚上都有點小墮落。以后要抓緊時間學習啦！
然后是課程方面的東西，還有兩個周就要相繼考試了，總共有3們課程！恩。。要抓緊時間啦！矩陣論+機器學習+圖像分析與計算機視覺！這些東西還是蠻多的！
然后這個周，就是把機器學習，算法，計算機視覺的那個給搞定！。。。
看了很多東西，昨天晚上看了一個臺灣交通大學的一個視頻，恩，挺不錯的！學到了很多東西。。我還需要繼續學習！
然后是實驗室的各種事情，靜下來好好做點東西。。。
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.
這個還是還是挺方便的。。