Gradient Boosted Decision Trees are an additive classification or regression model consisting of an ensemble of trees, fitted to current residuals, gradients of the loss function, in a forward step-wise manner. In the traditional boosting framework, the weak learners are generally shallow decision trees consisting of a few leaf nodes. GBDT ensembles are found to work well when there are hundreds of such decision trees. Gradient Boosted Decision Trees was introduced by Jerome Friedman in 1999.

http://www.doughellmann.com/PyMOTW/itertools/

Motivating example

We introduce the concept of maximum entropy through a simple example. Suppose we wish to model an expert translator's decisions concerning the proper French rendering of the English word in. Our model of the expert's decisions assigns to each French word or ph

import itertools, sys key_func=lambda x: x.split()[:1]; for k, g in itertools.groupby(sys.stdin, key_func): print k for i in g: print "["+i.strip()+"]";

http://www.zenspider.com/Languages/Ruby/QuickRef.html

1. capitalize

说明：
each——连续访问集合的所有元素
collect—-从集合中获得各个元素传递给block，block返回的结果生成新的集合。
map——-同collect。
inject——遍历集合中的各个元素，将各个元素累积成返回一个值。

例子：

Python 主函数传参数 import sys print len(sys.argv) print type(sys.argv) print str(sys.argv) for a in range(1, len(sys.argv)): print sys.argv[a] 参数为 ： sys.argv 和c++的差不多。注意文件名是第一个参数 xx.py

set hlsearch

http://www.ibm.com/developerworks/aix/library/au-gnuplot/

cygwin粘贴复制操作 收藏

http://www.aldenta.com/2006/09/15/plugin-bookmark-a-page-in-your-pdf/

With the long, yet wonderful, books in PDF format these days (Agile Web Development with Rails by Dave Thomas and David Heinemeier Hansson with Leon Breedt, Mike Clark,

This is an example of how to construct and search a kd-tree in Python with NumPy. kd-trees are e.g. used to search for neighbouring data points in multidimensional space. Searching the kd-tree for the nearest neighbour of all n points has O(n log n) complexity with respect to sample size.

http://www.scipy.org/Coo