def test_convert_is_date(self):
current_date = date.today()
current_date_converted = utility.convert(current_date)
self.assertIsNotNone(current_date_converted)
self.assertEquals(
current_date_converted,
str(current_date.year) + '-' + str(current_date.month).zfill(2) +
'-' + str(current_date.day).zfill(2))
def convert_params_to_correct_types(params):
converted_params = {}
for key, val in params.items():
new_val = utility.convert(val)
if type(
new_val
) == str: # If param is a comma separated string, convert it to a list of the elements
elements = new_val.split(",")
if len(elements) > 1:
new_val = [utility.convert(x) for x in elements]
if new_val[
-1] == '': # One-element string (nothing after the comma)
new_val = new_val[:-1]
converted_params[key] = new_val
return converted_params
def main():
ncolors = [256, 128, 64, 32, 16, 8, 4] #number of clusters
bw = [1, 2, 5, 10, 15, 40] #bandwidth values
img = Image()
img.read_img(
'/Users/davidclevenger/PycharmProjects/Pattern-Recognition/flowers120.ppm'
)
orig = img.pix
#k_means
print 'kmeans'
for i in ncolors:
tmp = convert(kmeans(i, orig))
img.pix = tmp
performance(i, orig, tmp)
img.write_img(
'/Users/davidclevenger/PycharmProjects/Pattern-Recognition/kmeans'
+ str(i) + '.ppm')
#winner_take_all
print 'winner_take_all'
for i in ncolors:
tmp = convert(winner_take_all(i, 0.1, orig))
img.pix = tmp
performance(i, orig, tmp)
img.write_img(
'/Users/davidclevenger/PycharmProjects/Pattern-Recognition/wta' +
str(i) + '.ppm')
#mean_shift
print 'mean_shift'
for i in bw:
tmp = convert(meanshift(i, orig))
img.pix = tmp
performance(i, orig, tmp)
img.write_img(
'/Users/davidclevenger/PycharmProjects/Pattern-Recognition/ms' +
str(i) + '.ppm')
return 0
def p_type(p):
'''type : BOOL
| CHAR
| BYTE
| SHORT
| INT
| LONG
| DOUBLE
| FLOAT
| ID '''
if not isType(p[1]):
raise SyntaxError
p[0] = convert(p[1])
def confusion_matrix(self, y_pred, y_real):
for i in range(len(y_pred)):
y_pred[i] = ut.convert(y_pred[i])
n = y_real.shape[1]
m = np.array(n * [n * [0]])
for i in range(len(y_real)):
if (y_real.values[i][0] == 1):
m[0] += y_pred[i]
elif (y_real.values[i][1] == 1):
m[1] += y_pred[i]
else:
m[2] += y_pred[i]
ut.plt_confusion_matrix(m)
return m
def _sample(self, size, training, random_state):
s = None
temp = None
if training:
temp = self.train_df.drop(pd.DataFrame())
else:
temp = self.test_df.drop(pd.DataFrame())
while len(temp) > 0:
if len(temp) < size:
s = temp.sample(len(temp), random_state=random_state)
temp = temp.drop(s.index)
s = utility.convert(s)
yield utility.reform(s)
else:
s = temp.sample(size, random_state=random_state)
temp = temp.drop(s.index)
s = utility.convert(s)
yield utility.reform(s)
def test_convert_is_none(self):
non_date = "I am not a date."
self.assertIsNone(utility.convert(non_date))
def p_acs(p):
''' acs : PRIVATE
| PUBLIC
| PROTECTED
| '''
addToFile(text=convert(p[1]) + " ")