musically-ut

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

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

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)