| # Mathieu Blondel, September 2010 | |
| # License: BSD 3 clause | |
| import numpy as np | |
| from numpy import linalg | |
| import cvxopt | |
| import cvxopt.solvers | |
| def linear_kernel(x1, x2): | |
| return np.dot(x1, x2) |
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亮亮（@ccwang002）| Mar, 2015 | CC 3.0 BY license
如果內容有誤，你可以用任何管道發訊息轟炸我，或用底下的 gist comment 留言。
每個檔案都會是一個主題，主題底下會列出一些資源。資源的最後會有一個學習目標，方便讓你評估自己學到什麼程度。學習目標會給一個明確的任務，我盡量讓它能跟（宅宅的）日常生活結合。通常只要完成前一、二個目標就行了，這也不是功課所以不一定要給我看。如果你不介意給我看，我會分享我主觀的建議，但大部份的任務是沒有絕對的正確答案。只要能解決問題都是好方法。
| # -*- coding: utf-8 -*- | |
| """ | |
| SCAN: A Structural Clustering Algorithm for Networks | |
| As described in http://ualr.edu/nxyuruk/publications/kdd07.pdf | |
| """ | |
| from collections import deque | |
| import numpy as np | |
| from scipy.sparse import csr_matrix |
| class A: | |
| def __init__(self): | |
| self.a = 1 | |
| @property | |
| def a(self): | |
| return 2 | |
| a = A() |
| #!/path/to/the/python/interpreter/in/virtualenv | |
| # -*- coding: utf-8 -*- | |
| import subprocess | |
| import sys | |
| def main(): | |
| try: | |
| subprocess.call("echo 'Hello!'", shell = True) | |
| sys.exit(0) |
| #include <stdio.h> | |
| typedef int (*myFun)(int x, int y); | |
| void helper(myFun afun){ | |
| afun(1, 1); | |
| } | |
| int fun(int x, int y){ | |
| printf("%d %d", x, y); |
| l = [[1,2,3,4,5], [5,6,7,50], [10,20,30]] | |
| i_max, (j_max, v_max) = max(enumerate([max(enumerate(ll), key = lambda t: t[1]) for ll in l]), key = lambda t: t[1][1]) | |
| # >>> i_max = 1, j_max = 3, v_max = 50 |
| class People: | |
| def __init__(self, name): | |
| self.name = name | |
| def __str__(self): | |
| return "Hello, I'm {}".format(self.name) | |
| def __repr__(self): | |
| return "<type People>: name = '{}'".format(self.name) |
| import Accelerate | |
| var realp = [Float]([1, 2, 3]) | |
| var imagp = [Float]([4, 5, 6]) | |
| var z = DSPSplitComplex(realp: &realp, imagp: &imagp) | |
| // vDSP_ctoz(x, 2, &z, 1, vDSP_Length(6)) | |
| for i in 0..<12 { | |
| print(z.realp[i]) |
| #!/usr/bin/env python | |
| # -*- coding: utf-8 -*- | |
| from __future__ import print_function | |
| import numpy as np | |
| import random | |
| """ | |
| Simple Back-Propagation Neural Network. | |
| Basic Usage: |