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# -*- coding: utf-8 -*- | |
import numpy as np | |
import scipy.stats as ss | |
import matplotlib.pyplot as plt | |
ss.binom.rvs(10, 0.3) | |
np.random.binomial(10, 0.3) |
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f[] := With[{p = RandomVariate[NormalDistribution[]]}, If[0 <= p, p, f[]]] | |
data = Table[f[], {249}]; (* no problem *) | |
data2 = Table[f[], {250}]; (* crash! *) | |
g[] := With[{p = RandomVariate[BinomialDistribution[10, 1/10]]}, If[0 <= p, p, g[]]] | |
data = Table[g[], {249}]; (* no problem *) |
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#include <stdio.h> | |
#define N 9 | |
#define B 3 //square root of N | |
int canBePlaced(int board[], int pos, int x) | |
{ | |
int row=pos/N; | |
int col=pos%N; | |
int i, j, topLeft; |
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0 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | |
0 | 0 | 10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 4 | 0 | |
0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | |
0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | |
0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | |
9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
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data = Import["https://gist.github.com/taroyabuki/4749183/raw/ffc84dbc4a3499ddef4a8c4f689b56c2948225bd/clustering_test_data.csv"]; | |
(* This returns no result *) | |
FindClusters[data, DistanceFunction -> CosineDistance, | |
Method -> {"Agglomerate", "Linkage" -> "Complete"}] | |
(* Shuffled data can be clustered. *) | |
FindClusters[RandomSample@data, DistanceFunction -> CosineDistance, | |
Method -> {"Agglomerate", "Linkage" -> "Complete"}] |
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samples <- data.frame( | |
x = c(3, 7, 10), | |
y = c(7, 1, 8), | |
row.names = c("A", "B", "C")) | |
plot(samples, type = "n") | |
text(samples[,1], samples[,2], row.names(samples)) | |
d <- dist(samples) |
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install.packages("RCurl") | |
install.packages("lsa") | |
library(RCurl) | |
url <- getURL("https://gist.github.com/taroyabuki/4749183/raw/ffc84dbc4a3499ddef4a8c4f689b56c2948225bd/clustering_test_data.csv", ssl.verifypeer = F) | |
data <- read.csv(textConnection(url), header = F) | |
library(lsa) | |
d <- dist(1 - cosine(t(as.matrix(data)))) | |
hr <- hclust(d, method = "complete") |
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Needs["HierarchicalClustering`"] | |
data = Import["https://gist.github.com/taroyabuki/4749183/raw/ffc84dbc4a3499ddef4a8c4f689b56c2948225bd/clustering_test_data.csv"]; | |
clusters = Agglomerate[data, DistanceFunction -> CosineDistance, Linkage -> "Complete"]; | |
On[Assert]; | |
Assert[Length@ClusterFlatten@clusters == Length@data] | |
{Length@ClusterFlatten@clusters, Length@data} |
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0.07832604499879574 | 0. | 0. | 0. | 0.2170723815877265 | 0. | 0. | 0. | 0. | 0. | 0. | 0. | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0. | 0. | 0. | 0. | 0. | 0.6432675209026769 | 0. | 0. | 0. | 0. | 0. | 0. | |
0. | 0. | 0. | 0. | 0. | 0. | 0. | 0. | 0.5601120336112039 | 0. | 0. | 0. | |
0. | 0. | 0. | 0. | 0. | 0. | 0. | 0. | 0. | 0. | 0.20965696734438366 | 0.24253562503633297 | |
0. | 0. | 0.5312532024908597 | 0.23791547571544325 | 0. | 0. | 0. | 0. | 0. | 0. | 0. | 0. | |
0. | 0.15961737689352443 | 0. | 0.15861031714362883 | 0. | 0. | 0. | 0. | 0. | 0. | 0. | 0. | |
0. | 0. | 0. | 0. | 0. | 0.5360562674188974 | 0. | 0. | 0. | 0. | 0. | 0. | |
0. | 0. | 0. | 0. | 0. | 0. | 0. | 0. | 0.42008402520840293 | 0. | 0. | 0. | |
0. | 0. | 0. | 0.4758309514308865 | 0. | 0. | 0. | 0. | 0. | 0. | 0. | 0. | |
0.15665208999759148 | 0.15961737689352443 | 0. | 0. | 0. | 0. | 0. | 0. | 0. | 0. | 0.20965696734438366 | 0.48507125007266594 |
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<?php | |
session_start(); | |
$_SESSION['consumer_key'] = 'Consumer Key'; | |
$_SESSION['consumer_secret'] = 'Consumer Secret'; | |
$callbackUrl = 'http://localhost/twitter/twitter-oauth-end.php'; | |
$oauth = new OAuth($_SESSION['consumer_key'], $_SESSION['consumer_secret'], OAUTH_SIG_METHOD_HMACSHA1, OAUTH_AUTH_TYPE_URI); | |
if (substr(PHP_OS, 0, 3) == 'WIN') {//XAMPP(いいかげん) | |
$caPath = 'C:/xampp/perl/vendor/lib/Mozilla/CA/'; |
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