Utkarsh Upadhyay musically-ut

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Nacreous Survivor: ransomed back from the Fathomking at great expense after winning.

musically-ut / Example code.R

Created February 2, 2016 14:31 — forked from anonymous/Example code

	function (eset, cl, mfrow = c(1, 1), colo, min.mem = 0, time.labels,
	new.window = TRUE)
	{
	clusterindex <- cl[[3]]
	memship <- cl[[4]]
	memship[memship < min.mem] <- -1
	colorindex <- integer(dim(exprs(eset))[[1]])
	if (missing(colo)) {
	colo <- c("#FF8F00", "#FFA700", "#FFBF00", "#FFD700",
	"#FFEF00", "#F7FF00", "#DFFF00", "#C7FF00", "#AFFF00",

musically-ut / Plotting_clustering_timeseries.R

Last active February 3, 2016 17:13 — forked from anonymous/Plotting_clustering_timeseries.R

Plot_cluster.R

	par(mfrows=c(clustering$cluster,1))

	for(j in 1:max(clustering$cluster))
	{
	x <- 1:12
	d <- d[clustering$cluster == j,]
	plot.default(x = NA, xlim = c(1, 12),
	ylim = c(min(y), max(y)), xlab = "Time", ylab = "Expression changes",
	main = paste("Cluster", j), axes = FALSE)

musically-ut / bench_semi_supervised_n_iter.py

Last active July 7, 2017 06:35 — forked from jnothman/bench_semi_supervised_n_iter

Benchmarking `sklearn.semi_supervised` `n_iter_` as a function of model and data characteristics

	import numpy as np
	from sklearn import datasets
	from sklearn.semi_supervised import LabelPropagation, LabelSpreading
	###for n_samples in [20, 200, 2000, 20000]:
	### X, y = datasets.make_classification(n_samples=n_samples, n_classes=3, n_informative=3)
	for (X, y) in [datasets.load_iris(return_X_y=True)]:
	for model in [LabelPropagation(max_iter=1000),
	#LabelSpreading(alpha=0.01),
	#LabelSpreading(alpha=0.1),
	#LabelSpreading(alpha=0.3)