def show_tsne(self, number_to_show):
plt_data = self.training_data[:number_to_show]
plt_labels = self.training_labels[:number_to_show]
for digit in range(10):
instances = [i for i in plt_labels if i == digit]
print("Digit {} appears {} times".format(digit, len(instances)))
transformer = TSNE(n_components=2, perplexity=40, verbose=2)
fig, plot = plt.subplots()
fig.set_size_inches(50, 50)
plt.prism()
X_transformed = transformer.fit_transform(plt_data)
plt.scatter(X_transformed[:, 0], X_transformed[:, 1], c=plt_labels)
plt.tight_layout()
count = 0
for label, x, y in zip(plt_labels, X_transformed[:, 0],
X_transformed[:, 1]):
if count % 100 == 0:
plt.annotate(str(int(label)),
xy=(x, y),
color='black',
weight='normal',
size=10,
bbox=dict(boxstyle='round4, pad=.5'))
count += 1
plt.savefig('mnist_digits.pdf')
def princicalComponentAnalysis():
# https://gist.github.com/mrgloom/6622175
# Explanation: https://lazyprogrammer.me/tutorial-principal-components-analysis-pca/
from sklearn.decomposition import PCA
X, y = mnist.data / 255., mnist.target
X_train, X_test = X[:60000], X[60000:]
y_train, y_test = y[:60000], y[60000:]
#X_train, y_train = shuffle(X_train, y_train)
#X_train, y_train = X_train[:1000], y_train[:1000] # lets subsample a bit for a first impression
pca = PCA(n_components=2, svd_solver='randomized')
#pca = PCA(n_components = 2)
fig, plot = plt.subplots()
fig.set_size_inches(50, 50)
plt.prism()
X_transformed = pca.fit_transform(X_train)
print(pca.explained_variance_ratio_)
plot.scatter(X_transformed[:, 0], X_transformed[:, 1], c=y_train)
plot.set_xticks(())
plot.set_yticks(())
plt.tight_layout()
plt.show()
def get_pairwise_plot(train_set_x,train_set_y):
fig, plots = plt.subplots(10, 10)
fig.set_size_inches(50, 50)
plt.prism()
pca = PCA()
for i in xrange(10):
for j in xrange(10):
class_i = i;
class_j = j;
class1_indexes=[index for index,value in enumerate(train_set_y) if value==class_i]
class2_indexes=[index for index,value in enumerate(train_set_y) if value==class_j]
class1_data = train_set_x[class1_indexes,:]
class2_data = train_set_x[class2_indexes,:]
pca.train(np.vstack((class1_data,class2_data)));
class1_proj=pca.project(class1_data)
plots[i, j].plot(class1_proj[:,0],class1_proj[:,1],'o',markersize=3,color='red',alpha=0.5,label=class_i)
if class_i!=class_j:
class2_proj=pca.project(class2_data)
plots[i, j].plot(class2_proj[:,0],class2_proj[:,1],'o',markersize=3,color='green',alpha=0.5,label=class_j)
plots[i, j].set_xticks(())
plots[i, j].set_yticks(())
return plt
def plot_pca(data, points, step, max_step, pca=True):
print(step, " ", max_step)
fig, plot = plt.subplots()
fig.set_size_inches(4, 4)
plt.prism()
#x_len = x_max - x_min
#y_len = y_max - y_min
#plt.plot([x_min + x_len*0.1, y_min + y_len*0.1], [x_min + x_len*0.8*(step / max_step), y_min + y_len*0.1])
plt.plot(data[:, 0],
data[:, 1],
'o',
markerfacecolor='grey',
markersize=1,
fillstyle='full',
markeredgewidth=0.0)
#colors = ['red', 'blue', 'green', 'purple', 'orange', 'teal', 'black', 'grey']
for i in range(len(points)):
plt.plot(points[i][0],
points[i][1],
'o',
markerfacecolor=colors[i],
markersize=6,
fillstyle='full',
markeredgewidth=0.0)
plot.set_xticks(())
plot.set_yticks(())
plt.title(str(int(step)))
plt.tight_layout(pad=-0.5, w_pad=-0.5, h_pad=-0.5)
#fig.savefig("plots/{}.pdf".format("pca" if pca else "t-sne"), bbox_inches='tight', pad_inches=0)
fig.savefig("plots/pca/{}_step_{}.png".format("pca" if pca else "t-sne",
step),
bbox_inches='tight',
pad_inches=0)
return fig
def scatter_plot(train_x, train_y, filename):
pca = PCA(train_x.shape[1], n_latent=2)
fig, plots = plot.subplots(10, 10)
fig.set_size_inches(50, 50)
plot.prism()
for i, j in product(xrange(10), repeat=2):
if i > j:
continue
X_ = train_x[(train_y == i) + (train_y == j)]
y_ = train_y[(train_y == i) + (train_y == j)]
# train on each pair of vars separately
pca.train(X_)
X_transformed = pca.apply(X_)
plots[i, j].scatter(X_transformed[:, 0], X_transformed[:, 1], c=y_)
plots[i, j].set_xticks(())
plots[i, j].set_yticks(())
plots[j, i].scatter(X_transformed[:, 0], X_transformed[:, 1], c=y_)
plots[j, i].set_xticks(())
plots[j, i].set_yticks(())
if i == 0:
plots[i, j].set_title(j)
plots[j, i].set_ylabel(j)
# plt.scatter(X_transformed[:, 0], X_transformed[:, 1], c=y_)
plot.tight_layout()
plot.savefig(filename)
def visualize_data (datafile):
#pca = RandomizedPCA(n_components=2)
au.log.info ('Linear Discriminant analysis')
lda = LDA(n_components=2)
fig, plots = plt.subplots(4, 4)
fig.set_size_inches(50, 50)
plt.prism()
for i, j in product(xrange(4), repeat=2):
if i > j:
continue
if i == j:
continue
X_ = X[(y == i) + (y == j)]
y_ = y[(y == i) + (y == j)]
#marks
#marks = y_.astype(str)
#marks[y_ == 0] = 'x'
#marks[y_ == 1] = 'o'
#marks[y_ == 2] = 'D'
#marks[y_ == 3] = '1'
#colors
colors = y_.copy()
colors[y_ == 0] = 0
colors[y_ == 1] = 1
colors[y_ == 2] = 2
colors[y_ == 3] = 3
#transform
#X_trans = pca.fit_transform(X_)
X_trans = lda.fit(X_, y_).transform(X_)
#plots
plots[i, j].scatter(X_trans[:, 0], X_trans[:, 1], c=colors, marker='o')
plots[i, j].set_xticks(())
plots[i, j].set_yticks(())
plots[j, i].scatter(X_trans[:, 0], X_trans[:, 1], c=colors, marker='o')
plots[j, i].set_xticks(())
plots[j, i].set_yticks(())
if i == 0:
plots[i, j].set_title (j)
plots[j, i].set_ylabel(j)
#plt.scatter(X_trans[:, 0], X_trans[:, 1], c=y_)
plt.tight_layout()
plt.savefig(outfile)
def visualize_data(datafile):
#pca = RandomizedPCA(n_components=2)
au.log.info('Linear Discriminant analysis')
lda = LDA(n_components=2)
fig, plots = plt.subplots(4, 4)
fig.set_size_inches(50, 50)
plt.prism()
for i, j in product(xrange(4), repeat=2):
if i > j:
continue
if i == j:
continue
X_ = X[(y == i) + (y == j)]
y_ = y[(y == i) + (y == j)]
#marks
#marks = y_.astype(str)
#marks[y_ == 0] = 'x'
#marks[y_ == 1] = 'o'
#marks[y_ == 2] = 'D'
#marks[y_ == 3] = '1'
#colors
colors = y_.copy()
colors[y_ == 0] = 0
colors[y_ == 1] = 1
colors[y_ == 2] = 2
colors[y_ == 3] = 3
#transform
#X_trans = pca.fit_transform(X_)
X_trans = lda.fit(X_, y_).transform(X_)
#plots
plots[i, j].scatter(X_trans[:, 0], X_trans[:, 1], c=colors, marker='o')
plots[i, j].set_xticks(())
plots[i, j].set_yticks(())
plots[j, i].scatter(X_trans[:, 0], X_trans[:, 1], c=colors, marker='o')
plots[j, i].set_xticks(())
plots[j, i].set_yticks(())
if i == 0:
plots[i, j].set_title(j)
plots[j, i].set_ylabel(j)
#plt.scatter(X_trans[:, 0], X_trans[:, 1], c=y_)
plt.tight_layout()
plt.savefig(outfile)
def theanoScatterPCA(path, dataset):
if dataset == 'mnist':
print('Loading Mnist Data')
(imageData, imageLabels) = LoadData.loadMNISTUnSplit(path,
shared=False)
print(imageData.shape)
elif dataset == 'cifar':
print('Loading Cifar Data')
(imageData, imageLabels) = LoadData.loadCIFAR10UnSplit(path,
shared=False)
imageData = imageData / 255.
print('Loaded')
print("Computing Scatter Plot")
labelIds = dict()
for idx in range(len(imageLabels)):
if str(imageLabels[idx]) not in labelIds:
labelIds[str(imageLabels[idx])] = []
labelIds[str(imageLabels[idx])].append(idx)
fig, plots = plt.subplots(10, 10)
fig.set_size_inches(50, 50)
plt.prism()
for i, j in product(xrange(10), repeat=2):
if i > j:
continue
idx = labelIds[str(i)] + labelIds[str(j)]
print('\tCalculating PCA For Classes %d And %d' % (i, j))
X_transformed = runPCA(data=imageData, elems=idx, components=2)
Y_ = imageLabels[labelIds[str(i)] + labelIds[str(j)]]
plots[i, j].scatter(X_transformed[:, 0], X_transformed[:, 1], c=Y_)
plots[i, j].set_xticks(())
plots[i, j].set_yticks(())
plots[j, i].scatter(X_transformed[:, 0], X_transformed[:, 1], c=Y_)
plots[j, i].set_xticks(())
plots[j, i].set_yticks(())
if i == 0:
plots[i, j].set_title(j)
plots[j, i].set_ylabel(j)
plt.tight_layout()
plt.savefig('scatter/' + dataset + ".png")
print("Computing Scatter Plot Finished")
def theanoScatterPCA(path, dataset):
if dataset == 'mnist':
print('Loading Mnist Data')
(imageData, imageLabels) = LoadData.loadMNISTUnSplit(path, shared=False)
print(imageData.shape)
elif dataset == 'cifar':
print('Loading Cifar Data')
(imageData, imageLabels) = LoadData.loadCIFAR10UnSplit(path, shared=False)
imageData = imageData / 255.
print('Loaded')
print("Computing Scatter Plot")
labelIds = dict()
for idx in range(len(imageLabels)):
if str(imageLabels[idx]) not in labelIds:
labelIds[str(imageLabels[idx])] = []
labelIds[str(imageLabels[idx])].append(idx)
fig, plots = plt.subplots(10, 10)
fig.set_size_inches(50, 50)
plt.prism()
for i, j in product(xrange(10), repeat=2):
if i > j:
continue
idx = labelIds[str(i)] + labelIds[str(j)]
print('\tCalculating PCA For Classes %d And %d' %(i,j))
X_transformed = runPCA(data=imageData, elems=idx, components=2)
Y_ = imageLabels[labelIds[str(i)] + labelIds[str(j)]]
plots[i, j].scatter(X_transformed[:, 0], X_transformed[:, 1], c=Y_)
plots[i, j].set_xticks(())
plots[i, j].set_yticks(())
plots[j, i].scatter(X_transformed[:, 0], X_transformed[:, 1], c=Y_)
plots[j, i].set_xticks(())
plots[j, i].set_yticks(())
if i == 0:
plots[i, j].set_title(j)
plots[j, i].set_ylabel(j)
plt.tight_layout()
plt.savefig('scatter/' + dataset + ".png")
print("Computing Scatter Plot Finished")
def plot_pca_mmr(data, build_orders, mmrs):
fig, plot = plt.subplots()
fig.set_size_inches(4, 4)
plt.prism()
x = data[:, 0]
y = data[:, 1]
min_mmr = np.min(mmrs)
max_mmr = np.max(mmrs)
c = cm.rainbow([norm(mmr, min_mmr, max_mmr) for mmr in mmrs])
for i in range(len(data)):
plt.scatter(x[i], y[i], color=c[i], s=2)
plot.set_xticks(())
plot.set_yticks(())
plt.tight_layout(pad=-0.5, w_pad=-0.5, h_pad=-0.5)
fig.savefig("plots/mds/mmr.pdf".format(),
bbox_inches='tight',
pad_inches=0)
return fig
def plot_2d(data, fitnesses, max_fit=1):
fig, plot = plt.subplots()
fig.set_size_inches(4, 4)
plt.prism()
for i in range(len(data)):
for p in range(len(data[i])):
size = 2 + (fitnesses[i][p] / max_fit) * 8
plt.plot(data[i][p][0],
data[i][p][1],
'o',
markerfacecolor=colors[i],
markersize=size,
fillstyle='full',
markeredgewidth=0.0)
plot.set_xticks(())
plot.set_yticks(())
plt.title("Archive")
fig.savefig(f"plots/archives/archive_{exp_id}.pdf",
bbox_inches='tight',
pad_inches=0)
return fig
def scatterplot(pca, x_train, y_train, n_classes, outputfile, imgsize=50):
"""Construct scatterplot of (x_train, y_train) data
:param pca: PCA object used for computing PCA on input data
:param x_train: input data
:param y_train: input labels
:param n_classes: number of classes of input data
:param imgsize: size of the img
:param outputfile: output file to save the plot
"""
fig, plots = plt.subplots(10, 10)
fig.set_size_inches(imgsize, imgsize)
plt.prism()
for i in range(n_classes):
for j in range(n_classes):
if i > j:
continue
print("Computing PCA for pair {}".format((i,j)))
x = numpy.asarray([x for (x, y) in zip(x_train, y_train) if y==i or y==j])
y = ([y for y in y_train if y==i or y==j])
x_pca = pca.compute_pca(x)
plots[i, j].scatter(x_pca[:, 0], x_pca[:, 1], c=y)
plots[i, j].set_xticks(())
plots[i, j].set_yticks(())
plots[j, i].scatter(x_pca[:, 0], x_pca[:, 1], c=y)
plots[j, i].set_xticks(())
plots[j, i].set_yticks(())
if i == 0:
plots[i, j].set_title(j)
plots[j, i].set_ylabel(j)
plt.tight_layout()
print("Saving figure...")
plt.savefig(outputfile)
def train(trainx, trainy, name):
x = tensor.matrix('x')
pca = PCA(x, components=2)
train_model = theano.function(
inputs=[x],
outputs=pca.output,
)
X_train, y_train = trainx / 255., trainy
print "pca running..."
fig, plots = plt.subplots(10, 10)
fig.set_size_inches(50, 50)
plt.prism()
for i, j in product(xrange(10), repeat=2):
if i > j:
continue
X_ = X_train[(y_train == i) + (y_train == j)]
y_ = y_train[(y_train == i) + (y_train == j)]
X_transformed = train_model(X_)
print "pca " , i, " and " , j
plots[i, j].scatter(X_transformed[:, 0], X_transformed[:, 1], c=y_)
plots[i, j].set_xticks(())
plots[i, j].set_yticks(())
plots[j, i].scatter(X_transformed[:, 0], X_transformed[:, 1], c=y_)
plots[j, i].set_xticks(())
plots[j, i].set_yticks(())
if i == 0:
plots[i, j].set_title(j)
plots[j, i].set_ylabel(j)
plt.tight_layout()
plt.savefig(name)
return (basin, (bigEnough or not willBeConsideredAgain))
# State variable for _capture() for caching.
_capture._markedSoFar = {}
if __name__ == '__main__' :
#from RandomImage import RandomImage
#np.random.seed(32)
#i = RandomImage(200, 50, 95, (1000, 1000))
from scipy.ndimage import imread, gaussian_filter
i = imread("/home/bvr/SatData/2011.07.01.12.00.png")
i = gaussian_filter(i, 1)[300:800, 300:i.shape[1]/2]
print "Shape:", i.shape, " DType:", i.dtype
globs, basins = Watershed_Transform(i, 5, 250)
print "Glob Cnt:", len(globs)
#import cProfile
#cProfile.run("Watershed_Transform(i, 40, 1000)", "watershed_lg5_profile")
basins = np.ma.masked_array(basins, mask=(basins <= 0))
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(1, 2, 1)
ax.imshow(i, cmap=plt.gray(), interpolation='none')
ax = fig.add_subplot(1, 2, 2)
ax.imshow(basins, vmin=0, cmap=plt.prism(), interpolation='none')
plt.show()
X_train.append(XX_train[i])
y_train.append(yy_train[i])
num_samples_to_plot = 5000
X_train, y_train = shuffle(X_train, y_train)
X_train, y_train = X_train[:
num_samples_to_plot], y_train[:
num_samples_to_plot] # lets subsample a bit for a first impression
for digit in mytargets:
instances = [i for i in y_train if i == digit]
print "Digit", digit, "appears ", len(instances), "times"
transformer = Isomap(n_neighbors=10, n_components=2)
fig, plot = plt.subplots()
fig.set_size_inches(50, 50)
plt.prism()
X_transformed = transformer.fit_transform(X_train)
plot.scatter(X_transformed[:, 0], X_transformed[:, 1], c=y_train)
plot.set_xticks(())
plot.set_yticks(())
count = 0
plt.tight_layout()
plt.suptitle("Isomap for MNIST digits ")
for label, x, y in zip(y_train, X_transformed[:, 0], X_transformed[:, 1]):
#Lets annotate every 1 out of 200 samples, otherwise graph will be cluttered with anotations
if count % 200 == 0:
plt.annotate(str(int(label)),
xy=(x, y),
color='black',
import numpy as np
import matplotlib.pyplot as pl
from sklearn.utils import shuffle
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
#Import dataset
red = np.loadtxt("./red.txt")
blue = np.loadtxt("./blue.txt")
#Plot data
pl.prism()
plt.xlim(-1.2, 1.2)
plt.ylim(-1.2, 1.2)
pl.scatter(red[:, 0], red[:, 1], c='red')
pl.scatter(blue[:, 0], blue[:, 1], c='blue')
#Prepare data for analysis
reds = np.hstack ((red, [[1]] * len (red) ))
blues = np.hstack ((blue, [[0]] * len (blue) ))
dots = np.concatenate((reds, blues), axis=0)
x = dots[:, :-1]
y = dots[:, 2]
#Train and test sets
x, y = shuffle(x, y, random_state=1)
size=dots.shape[0] * 0.8
x_train = x[:size]
y_train = y[:size]
x_test = x[size:]
y_test = y[size:]
def do_oasis_visualize_pca(args):
subjsf = args.infile.strip()
outfile = args.outfile.strip()
datadir = args.datadir.strip()
maskf = args.mask.strip()
fsmethod = args.fsmethod.strip()
# scale = args.scale
# scale_min = args.scale_min
# scale_max = args.scale_max
verbose = args.verbosity
# logging config
au.setup_logger(verbose)
# loading mask
msk = nib.load(maskf).get_data()
nvox = np.sum(msk > 0)
indices = np.where(msk > 0)
# reading subjects list
[scores, subjs] = parse_subjects_list(subjsf, datadir)
scores = np.array(scores)
imgsiz = nib.load(subjs[0]).shape
nsubjs = len(subjs)
# checking mask and first subject dimensions match
if imgsiz != msk.shape:
au.log.error("Subject image and mask dimensions should coincide.")
exit(1)
# relabeling scores to integers, if needed
if not np.all(scores.astype(np.int) == scores):
unis = np.unique(scores)
scs = np.zeros(scores.shape, dtype=int)
for k in np.arange(len(unis)):
scs[scores == unis[k]] = k
y = scs.copy()
else:
y = scores.copy()
# loading data
au.log.info("Loading data...")
X = np.zeros((nsubjs, nvox), dtype="float32")
for f in np.arange(nsubjs):
imf = subjs[f]
au.log.info("Reading " + imf)
img = nib.load(imf).get_data()
X[f, :] = img[msk > 0]
# demo
"""
from sklearn.datasets import fetch_mldata
mnist = fetch_mldata("MNIST original")
X, y = mnist.data[:60000] / 255., mnist.target[:60000]
X, y = shuffle(X, y)
X, y = X[:5000], y[:5000] # lets subsample a bit for a first impression
"""
# lets start plotting
au.log.info("Preparing plots...")
X, y = shuffle(X, y)
X = X / X.max()
# reducing training and test data
if fsmethod != "none":
au.log.info("Feature selecion : " + fsmethod)
selector = select_features(X, y, fsmethod)
X = selector.transform(X)
# au.log.info ('Randomized PCA')
# pca = RandomizedPCA(n_components=2)
au.log.info("Linear Discriminant analysis")
lda = LDA(n_components=2)
fig, plots = plt.subplots(4, 4)
fig.set_size_inches(50, 50)
plt.prism()
for i, j in product(xrange(4), repeat=2):
if i > j:
continue
if i == j:
continue
X_ = X[(y == i) + (y == j)]
y_ = y[(y == i) + (y == j)]
# marks
# marks = y_.astype(str)
# marks[y_ == 0] = 'x'
# marks[y_ == 1] = 'o'
# marks[y_ == 2] = 'D'
# marks[y_ == 3] = '1'
# colors
colors = y_.copy()
colors[y_ == 0] = 0
colors[y_ == 1] = 1
colors[y_ == 2] = 2
colors[y_ == 3] = 3
# transform
# X_trans = pca.fit_transform(X_)
X_trans = lda.fit(X_, y_).transform(X_)
# plots
plots[i, j].scatter(X_trans[:, 0], X_trans[:, 1], c=colors, marker="o")
plots[i, j].set_xticks(())
plots[i, j].set_yticks(())
plots[j, i].scatter(X_trans[:, 0], X_trans[:, 1], c=colors, marker="o")
plots[j, i].set_xticks(())
plots[j, i].set_yticks(())
if i == 0:
plots[i, j].set_title(j)
plots[j, i].set_ylabel(j)
# plt.scatter(X_trans[:, 0], X_trans[:, 1], c=y_)
plt.tight_layout()
plt.savefig(outfile)
def plot_pca(data,
build_orders,
pca=False,
eps=0.3,
min_samples=10,
min_clusters=1):
fig, plot = plt.subplots()
fig.set_size_inches(4, 4)
plt.prism()
#plot.scatter(data[...,0][:-5], data[...,1][:-5], c=all_colors[:-5])
#s = [100 for n in range(5)]
#plot.scatter(data[...,0][-5:], data[...,1][-5:], c=all_colors[-5:], marker='s', s=s)
# Clustering
db = DBSCAN(eps=eps, min_samples=min_samples).fit(data)
core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
core_samples_mask[db.core_sample_indices_] = True
labels = db.labels_
# Number of clusters in labels, ignoring noise if present.
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
print('Estimated number of clusters: %d' % n_clusters_)
if n_clusters_ < min_clusters:
return
# Black removed and is used for noise instead.
unique_labels = set(labels)
colors = [
plt.cm.Spectral(each) for each in np.linspace(0, 1, len(unique_labels))
]
for k, col in zip(unique_labels, colors):
if k == -1:
# Black used for noise.
col = [0, 0, 0, 1]
class_member_mask = (labels == k)
xy = data[class_member_mask & core_samples_mask]
plt.plot(xy[:, 0],
xy[:, 1],
'o',
markerfacecolor=tuple(col),
markersize=3,
fillstyle='full',
markeredgewidth=0.0)
# Find centroids
if len(xy) > 0:
center = (sum(xy[:, 0]) / len(xy), sum(xy[:, 1]) / len(xy))
centroid = closest_point(center, xy)
centroid_idx = -1
assert centroid in data
for i in range(len(data)):
if np.array_equal(data[i], centroid):
centroid_idx = i
break
assert centroid_idx >= 0
print("Centroid of cluster {} with color {} and position {},{}".
format(k, col, centroid[0], centroid[1]))
print(build_orders[centroid_idx])
plt.plot(centroid[0],
centroid[1],
'o',
markerfacecolor=tuple(col),
markeredgecolor='k',
markersize=8)
bbox_props = dict(boxstyle="circle,pad=0.1",
fc="white",
ec="black",
lw=1)
t = plot.text(centroid[0],
centroid[1],
k,
ha="center",
va="center",
rotation=0,
size=6,
bbox=bbox_props)
xy = data[class_member_mask & ~core_samples_mask]
plt.plot(xy[:, 0],
xy[:, 1],
'o',
markerfacecolor=tuple(col),
markersize=3,
fillstyle='full',
markeredgewidth=0.0)
plot.set_xticks(())
plot.set_yticks(())
'''
custom_lines = [Line2D([0], [0], marker='o', color='w', label='Scatter',
markerfacecolor='red', markersize=4),
Line2D([0], [0], marker='o', color='w', label='Scatter',
markerfacecolor='white', markeredgecolor='black', markersize=4)]
'''
#for i in range(len(human_levels)):
# plot.annotate("Level {}".format(i), (data[len(gen_levels)+i][0], data[len(gen_levels)+i][1]))
'''
for i in range(len(human_levels)):
bbox_props = dict(boxstyle="circle,pad=0.1", fc="white", ec="black", lw=2)
t = plot.text(data[len(gen_levels)+i][0], data[len(gen_levels)+i][1], i, ha="center", va="center", rotation=0,
size=15,
bbox=bbox_props)
'''
plt.tight_layout(pad=-0.5, w_pad=-0.5, h_pad=-0.5)
#plot.margins(0, 0)
#lines = ax.plot(data)
title = "PCA" if pca else "t-SNE"
#plt.title(title)
#plot.legend(custom_lines, ['PCG', 'Human'], loc=1)
#plot.legend(['Won', 'Lost', 'Human'], loc=2)
fig.savefig("plots/mds/{}_eps-{}_sam-{}.pdf".format(
"pca" if pca else "t-sne", eps, min_samples),
bbox_inches='tight',
pad_inches=0)
return fig
def visualize_dataset(X, y, ih, method='both'):
if method == 'both': # Draw both in same plot
plt.figure(figsize=(4, 9))
plt.subplots_adjust(bottom=.05, top=.9, left=.05, right=.95)
plt.subplot(2, 1, 1)
plt.title("MDS of %s" % dataset_name[:-4])
mds = MDS(n_components=2, max_iter=100, n_init=1)
X_transformed = mds.fit_transform(X)
y[y == 0] = -1
# color (blue/red) = sign: will reflect class, prominence = magnitude: will reflect IH. Added 0.2 so that y*IH
# doesn't become zero (thus making colors/classes indistinguishable) effectively shifting the IH range from (0,1) to (0.2, 1.2)
plt.scatter(X_transformed[:, 0],
X_transformed[:, 1],
s=5,
c=y * (ih + 0.1),
vmin=-1,
vmax=1,
cmap=plt.cm.coolwarm)
plt.subplot(2, 1, 2)
plt.title("t-SNE of %s" % dataset_name[:-4])
tsne = TSNE(n_components=2)
X_transformed = tsne.fit_transform(X)
yy = y
yy[yy == 0] = -1
# color (blue/red) = sign: will reflect class, prominence = magnitude: will reflect IH. Added 0.2 so that y*IH
# doesn't become zero (thus making colors/classes indistinguishable) effectively shifting the IH range from (0,1) to (0.2, 1.2)
plt.scatter(X_transformed[:, 0],
X_transformed[:, 1],
s=5,
c=yy * (ih + 0.1),
vmin=-1,
vmax=1,
cmap=plt.cm.coolwarm)
figname = 'visualizations//' + dataset_name[:-4] + '.png'
plt.savefig(figname, bbox_inches='tight')
return
else: # Plot either MDS or t-SNE
if (method == 'MDS'):
transformer = MDS(n_components=2, max_iter=100, n_init=1)
elif method == 't-SNE': # t-SNE
transformer = TSNE(n_components=2)
fig, plot = plt.subplots()
fig.set_size_inches(8, 8)
plt.prism()
# Blue for low (good) mRS-90, Red for High
colors = ['blue', 'red']
X_transformed = transformer.fit_transform(X)
y[y == 0] = -1
# color (blue/red) = sign: will reflect class, prominence = magnitude: will reflect IH. Added 0.2 so that y*IH
# doesn't become zero (thus making colors/classes indistinguishable) effectively shifting the IH range from (0,1) to (0.2, 1.2)
plot.scatter(X_transformed[:, 0],
X_transformed[:, 1],
c=y * (ih + 0.2),
vmin=-1,
vmax=1,
cmap=plt.cm.coolwarm)
plot.set_xticks(())
plot.set_yticks(())
count = 0
plt.tight_layout()
if (method == 'MDS'):
title = "MDS of " + dataset_name + " dataset"
# title = "MDS"
plt.suptitle(title, fontsize=20)
else:
title = "t-SNE of " + dataset_name + " dataset"
# title = "t-SNE"
plt.suptitle(title, fontsize=20)
plt.show()
return
for i, label in enumerate(yy_train):
if label in mytargets:
X_train.append(XX_train[i])
y_train.append(yy_train[i])
num_samples_to_plot = 5000
X_train, y_train = shuffle(X_train, y_train)
X_train, y_train = X_train[:num_samples_to_plot], y_train[:num_samples_to_plot] # lets subsample a bit for a first impression
for digit in mytargets:
instances=[i for i in y_train if i==digit]
print "Digit",digit,"appears ",len(instances), "times"
transformer = Isomap(n_neighbors = 10, n_components = 2)
fig, plot = plt.subplots()
fig.set_size_inches(50, 50)
plt.prism()
X_transformed = transformer.fit_transform(X_train)
plot.scatter(X_transformed[:, 0], X_transformed[:, 1], c=y_train)
plot.set_xticks(())
plot.set_yticks(())
count=0;
plt.tight_layout()
plt.suptitle("Isomap for MNIST digits ")
for label , x, y in zip(y_train, X_transformed[:, 0], X_transformed[:, 1]):
#Lets annotate every 1 out of 200 samples, otherwise graph will be cluttered with anotations
if count % 200 == 0:
plt.annotate(str(int(label)),xy=(x,y), color='black', weight='normal',size=10,bbox=dict(boxstyle="round4,pad=.5", fc="0.8"))
count = count + 1
#plt.savefig("mnist_pca.png")
return (basin, (bigEnough or not willBeConsideredAgain))
# State variable for _capture() for caching.
_capture._markedSoFar = {}
if __name__ == '__main__':
#from RandomImage import RandomImage
#np.random.seed(32)
#i = RandomImage(200, 50, 95, (1000, 1000))
from scipy.ndimage import imread, gaussian_filter
i = imread("/home/bvr/SatData/2011.07.01.12.00.png")
i = gaussian_filter(i, 1)[300:800, 300:i.shape[1] / 2]
print "Shape:", i.shape, " DType:", i.dtype
globs, basins = Watershed_Transform(i, 5, 250)
print "Glob Cnt:", len(globs)
#import cProfile
#cProfile.run("Watershed_Transform(i, 40, 1000)", "watershed_lg5_profile")
basins = np.ma.masked_array(basins, mask=(basins <= 0))
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(1, 2, 1)
ax.imshow(i, cmap=plt.gray(), interpolation='none')
ax = fig.add_subplot(1, 2, 2)
ax.imshow(basins, vmin=0, cmap=plt.prism(), interpolation='none')
plt.show()