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v.py
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v.py
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import datetime, os, time
import numpy as np
import pylab as pl
from scipy.sparse import csr_matrix
from sklearn.metrics.pairwise import pairwise_distances
from sklearn.decomposition import KernelPCA
from sklearn.utils.graph import graph_shortest_path
def kneighbors(X, n_neighbors, metric):
"""Finds the K-neighbors of a point.
Based on sklearn
Returns distance ind
"""
if metric == 'abs_correlation':
dist = pairwise_distances(X, metric = 'correlation')
dist = np.sqrt( -np.log( np.abs(1 - dist )) )
else:
dist = pairwise_distances(X, metric = metric)
neigh_ind = dist.argsort(axis=1)
neigh_ind = neigh_ind[:, :n_neighbors]
return neigh_ind
#
def kneighbors_graph(X, n_neighbors, metric):
"""Computes the (weighted) graph of k-Neighbors for points in X.
Based on sklearn
"""
X = np.asarray(X)
n_samples1 = n_samples2 = X.shape[0]
n_nonzero = n_samples1 * n_neighbors
A_indptr = np.arange(0, n_nonzero + 1, n_neighbors)
# construct CSR matrix representation of the k-NN graph
A_data = np.ones((n_samples1, n_neighbors))
A_ind = kneighbors(X, n_neighbors, metric)
return csr_matrix((A_data.ravel(), A_ind.ravel(), A_indptr), shape=(n_samples1, n_samples2))
def isomap(X, n_neighbors, metric):
"""
Based on sklearn,
Author: Jake Vanderplas -- <vanderplas@astro.washington.edu>
License: BSD, (C) 2011
"""
kng = kneighbors_graph(D, n_neighbors = n_neighbors, metric = metric)
dist_matrix_ = graph_shortest_path(kng, method='auto', directed=False)
kernel_pca_ = KernelPCA(n_components=2, kernel="precomputed", eigen_solver='auto')
G = dist_matrix_ ** 2
G *= -0.5
return kernel_pca_.fit_transform(G)
X = np.array( [pl.imread('frames/' + f).flatten() for f in sorted(os.listdir("frames"))], dtype = np.float64 )
SW = np.random.permutation(X.shape[1])
X = X[:, SW] # permute pixels
#D = np.array([np.concatenate((pixel, pixel[i%2:-i%2 or None], pixel[(i+1)%2:(i+1)%2 or None])) for i,pixel in enumerate(X.T)])
D = X.T
for metric in ['abs_correlation', 'braycurtis', 'canberra', 'correlation', 'cosine', 'minkowski', 'seuclidean']:
for n_neighbors in [4,5,6]:
pl.figure()
results = isomap(D, n_neighbors, metric = metric)
x,y = results.real[:,0], results.real[:,1]
pl.scatter(x, y, c = np.arange(X.shape[1])[SW], cmap=pl.jet())
pl.title("%s n_neighbors %d" % (metric, n_neighbors))
#pl.savefig("%s.n_neighbors.%d.png" % (metric, n_neighbors))
pl.show()
exit()
print "Saving restored frames..."
name = "movie"
if not os.path.exists(name): os.mkdir(name)
for i in range(len(X)):
pl.figure(figsize=(2, 2))
pl.hexbin(x, y, gridsize = 50, C = X[i], cmap=pl.gray())
pl.savefig("%s/frame.%04d.png" % (name, i))
pl.close()
print ".",
print "Done."
print "Creating movie..."
os.system("ffmpeg -y -f image2 -i %s/frame.%%04d.png -r 12 %s.mov" % (name, name))