def main():
# create_date_set(lambda x: np.sin(x), np.linspace(0, 10, 10, endpoint=True), "sin.txt")
# x, y = read_data("sin.txt")
# a, b, c, d = spline.get_coefficients(x, y)
# show(x, y, b, c, d, lambda t: np.sin(t))
func_num = utils.choose_from_map(functions)
section = utils.read_section("Введите интервал, используя разделитель ;",
";")
n = utils.read_int(
0, 100, "Укажите количество узлов для вычисления уравнения сплайнов")
x = np.linspace(section[0], section[1], n, endpoint=True)
y = functions[func_num][1](x)
a, b, c, d = spline.get_coefficients(x, y)
show(x, y, b, c, d, functions[func_num][1])
import numpy as np
from keras.models import load_model
import cv2
import os
from utils import file_generator
from utils import read_section
properties = read_section("part4.ini", "part4")
model_dir = properties["model.save.dir"]
model_file = properties["model.save.name"]
faces_out_dir = properties["real.faces.out.dir"]
not_faces_out_dir = properties["not.real.faces.out.dir"]
src_dir = properties["faces.dir"]
model = load_model(model_dir + model_file)
def main():
out_dir = [not_faces_out_dir, faces_out_dir]
for file_name in file_generator(src_dir):
try:
img = cv2.imread(file_name)
img = img.reshape(1, img.shape[0], img.shape[1], img.shape[2])
a = model.predict(img)
os.rename(file_name,
out_dir[int(a[0][0])] + file_name[len(src_dir):])
except Exception as e:
print(str(e))
import numpy as np
import os
import cv2
from utils import read_section
properties = read_section("part3.ini", "part3")
input_dir = properties["dataset.dir"]
input_X = input_dir + properties["dataset.file.name.x"]
input_y = input_dir + properties["dataset.file.name.y"]
def load_data():
X = np.load(input_X)
y = np.load(input_y)
size = y.shape[0]
indices = np.random.permutation(size)
eighty = (int)(size*80/100)
training_idx, test_idx = indices[:eighty], indices[eighty:]
training_X, test_X = X[training_idx,:,:], X[test_idx,:,:]
training_y, test_y = y[training_idx], y[test_idx]
return (training_X, test_X), (training_y, test_y)
import numpy as np
import os
import cv2
from utils import read_section
from utils import file_generator
properties = read_section("part2.ini", "part2")
faces_dir = properties["input.images.faces"]
other_dir = properties["input.images.other"]
extension = properties["images.extension"]
image_height = int(properties["face.size.height"])
image_width = int(properties["face.size.width"])
output_dir = properties["output.dataset.dir"]
output_X = output_dir + properties["dataset.file.name.x"]
output_y = output_dir + properties["dataset.file.name.y"]
def read_dir(ar, y, dir, cur_class):
a = 0
for filename in file_generator(dir):
try:
img = cv2.imread(filename)
if(ar.shape[0] == 1 and a == 0):
ar[0,:,:,:] = img
a = 1
else:
tmp = np.zeros((1,image_height,image_width,3))
tmp[0,:,:,:] = img
ar = np.concatenate((ar, tmp), axis=0)
y = np.append(y, cur_class)
except Exception as e:
import streamingDataset as sd
import numpy as np
np.random.seed(2808)
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.utils import np_utils
from keras import backend as K
from utils import read_section
from keras.optimizers import Adam
properties = read_section("part5.ini", "part5")
dirs = [properties["real.faces.dir"], properties["not.real.faces.dir"]]
classes = [1, 0]
dataset = sd.StreamingDataset(dirs, classes)
model_dir = properties["model.save.dir"]
model_file = properties["model.save.name"]
'''
(X_train, X_test), (y_train, y_test) = load_data()
if K.image_data_format() == 'channels_first':
X_train = X_train.reshape(X_train.shape[0], X_train.shape[3], X_train.shape[1], X_train.shape[2])
X_test = X_test.reshape(X_test.shape[0], X_test.shape[3], X_test.shape[1], X_test.shape[2])
input_shape = (X_train.shape[3], X_train.shape[1], X_train.shape[2])
else:
input_shape = (X_train.shape[1], X_train.shape[2], X_train.shape[3])
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
if x == 1:
return 0
return (x**3 - x**2) / (x - 1)
integral = u'\u222b'
integrals = ((integral + " x^2 dx", lambda x: x**2),
(integral + " x^3 dx", lambda x: x**3), (integral + " sin(x) dx",
lambda x: np.sin(x)),
(integral + " x^3-x^2+3 dx", lambda x: x**3 - x**2 + 3),
(integral + " 1/x dx", lambda x: one_for_x(x)),
(integral + " ln(x) dx", lambda x: lnx(x)),
(integral + " sin(x)/x dx",
lambda x: sinx_x(x)), (integral + " (x^3-x^2)/(x-1) dx",
lambda x: x3_x2_div_x1(x)))
print("Выберите какой интеграл посчитать")
integral_num = ut.choose_from_map(integrals)
section = ut.read_section("Введите интервал используя разделитель ;", ";")
epsilon = ut.read_float(0, 100, "Введите эпсилон")
try:
result, inaccuracy, new_n = integral_solve_symp.solve_with_eps_sympson(
section[0], section[1], 5, integrals[integral_num][1], epsilon)
print("Результат работы метода Симпсона: " + str(result))
print("Погрешность вычисления метода Симпсона: " + str(inaccuracy))
print("n: " + str(new_n))
except ValueError as e:
print(
"Произошла ошибка при вычислении значения функции на промежутке, Adios"
)
sys.exit()
for i in range(len(x)):
plt.scatter(x[i], y[i])
for point in points:
plt.scatter(point[0], point[1])
plt.plot(fun_x, spline_y, label='Вычисленная функция')
plt.grid()
plt.legend()
plt.hlines(0, x[0], x[len(x) - 1], color="black")
if x[0] * x[1] <= 0:
plt.vlines(0, plt.ylim()[0], plt.ylim()[1], color="black")
plt.show()
if __name__ == '__main__':
func_num = utils.choose_from_map(functions)
x0, x_last = utils.read_section("Введите промежуток используя ;", ";")
inaccuracy = utils.read_float(
0, 100, "Введите точность, с которой будет вычислено значение")
y0 = utils.read_float(-1000.0, 1000.0,
"Введите значение у0 в левой границе отрезка")
try:
c_integral = functions_c[func_num][1](x0, y0)
x, y = runge.calculate_data_set(x0, y0, x_last, functions[func_num][1],
functions_integral[func_num][1],
c_integral, 0.1)
# print(x)
# print(y)
a, b, c, d = spline.get_coefficients(x, y)
show(x, y, b, c, d, functions_integral[func_num][1], c_integral)
except Exception:
print(