def TSNE_Gist(name, csvfilename):
idsT = imagesHandler.get_all_img_ids()
ids = []
for id in idsT:
ids.append(str(id[0]))
print ids
gistVals = util.loadCSV(csvfilename)
X = np.array(gistVals)
model = TSNE(n_components=2, random_state=0)
tsne_vals = model.fit_transform(X)
tsneHandler.storeTsneValsWIds(name, tsne_vals, ids)
return tsne_vals, ids
def TSNE_Gist(name, csvfilename):
idsT = imagesHandler.get_all_img_ids()
ids = []
for id in idsT:
ids.append(str(id[0]))
print ids
gistVals = util.loadCSV(csvfilename)
X = np.array(gistVals)
model = TSNE(n_components=2, random_state=0)
tsne_vals = model.fit_transform(X)
tsneHandler.storeTsneValsWIds(name, tsne_vals, ids)
return tsne_vals, ids
def TSNE_sift(name):
conn = sqlite3.connect(dirm.sqlite_file)
c = conn.cursor()
dist = sift_cb_handler.get_distributions()
X_Ids = []
X_data = []
for d in dist:
x_id = d[0]
x_data = d[1:]
X_Ids.append(x_id)
X_data.append(x_data)
X_data = np.array(X_data)
model = TSNE(n_components=2)
tsne_x = model.fit_transform(X_data)
tsneHandler.storeTsneValsWIds(name, tsne_x, X_Ids)
return tsne_x, X_Ids
def TSNE_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)
model = TSNE(n_components=2, random_state=0)
tsne_vals = model.fit_transform(X)
tsneHandler.storeTsneValsWIds(tablename, tsne_vals, ids)
return tsne_vals, ids
def TSNE_sift(name):
conn = sqlite3.connect(dirm.sqlite_file)
c = conn.cursor()
dist = sift_cb_handler.get_distributions()
X_Ids = []
X_data = []
for d in dist:
x_id = d[0]
x_data = d[1:]
X_Ids.append(x_id)
X_data.append(x_data)
X_data = np.array(X_data)
model = TSNE(n_components=2)
tsne_x = model.fit_transform(X_data)
tsneHandler.storeTsneValsWIds(name, tsne_x, X_Ids)
return tsne_x, X_Ids
def TSNE_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)
model = TSNE(n_components=2, random_state=0)
tsne_vals = model.fit_transform(X)
tsneHandler.storeTsneValsWIds(tablename, tsne_vals, ids)
return tsne_vals, ids
