def main():
filenm = "surf3"
# tsne_x,X_Ids = TSNE_Surf(filenm)
Tsne, ids = tsneHandler.getTSNEwithIds(filenm)
img = plot_visualisation.plot_raw_TSNE(Tsne, ids, filenm)
# img = cv2.imread(dirm.outputDirectory+"surf3.jpg")
plot_visualisation.displayInteractiveImage(img, "surf3")
def main():
filenm = "surf3"
#tsne_x,X_Ids = TSNE_Surf(filenm)
Tsne, ids = tsneHandler.getTSNEwithIds(filenm)
img = plot_visualisation.plot_raw_TSNE(Tsne, ids, filenm)
#img = cv2.imread(dirm.outputDirectory+"surf3.jpg")
plot_visualisation.displayInteractiveImage(img, "surf3")
def Kmenas_General(tablename):
conn = sqlite3.connect(dirm.sqlite_file)
c = conn.cursor()
cmd = 'SELECT * FROM {tn}'.format(tn=tablename)
c.execute(cmd)
all_rows = c.fetchall()
ids = []
data = []
for row in all_rows:
ids.append(str(row[0]))
data.append(row[1:])
X = np.array(data)
estimators = {
'k_means_3': KMeans(n_clusters=3),
'k_means_5': KMeans(n_clusters=3),
'k_means_8': KMeans(n_clusters=8)
}
est = KMeans(n_clusters=3)
est.fit(X)
labels = est.labels_
labels_np = np.array(labels)
results = []
tsne_vals, ids2 = tsneHandler.getTSNEwithIds(tablename)
tsne_x, tsne_y = zip(*tsne_vals)
results = zip(ids, ids2, tsne_x, tsne_y, labels_np)
#header = ('id','id2','tsne_x','tsne_y','lables')
#results_w_header = header + results
#print results_w_header
util.writetoCSV(results, tablename + "_clustered")
def Kmenas_General(tablename):
conn = sqlite3.connect(dirm.sqlite_file)
c = conn.cursor()
cmd = 'SELECT * FROM {tn}'.format(tn=tablename)
c.execute(cmd)
all_rows = c.fetchall()
ids = []
data = []
for row in all_rows:
ids.append(str(row[0]))
data.append(row[1:])
X = np.array(data)
estimators = {'k_means_3': KMeans(n_clusters=3),'k_means_5':KMeans(n_clusters=3),'k_means_8': KMeans(n_clusters=8)}
est = KMeans(n_clusters=3)
est.fit(X)
labels = est.labels_
labels_np = np.array(labels)
results = []
tsne_vals,ids2 = tsneHandler.getTSNEwithIds(tablename)
tsne_x,tsne_y = zip(*tsne_vals)
results = zip(ids,ids2,tsne_x,tsne_y,labels_np)
#header = ('id','id2','tsne_x','tsne_y','lables')
#results_w_header = header + results
#print results_w_header
util.writetoCSV(results, tablename + "_clustered")
#get filename
fs = imageIDs
N = len(fs) #N = length(fs);
#size of final image
S = 10008;
G = np.zeros((S,S,3)) # ,'uint8'
#print G
# size of every image thumbnail
s = 278
xnum = S/s
ynum = S/s
used = np.zeros((N, 1))
qq=len(range(0,S,s))
abes = np.zeros((qq*2,2))
print len(abes)
i=0
for a in range(0,S,s):
for b in range(0,S,s):
abes[i,:] = [a,b]
i=i+1;
print abes
tsne_vals,ids = tsneHandler.getTSNEwithIds("colourDistribution")
plotCompTSNE(tsne_vals, ids, "colourPlot")