def init_theta(X, Y):
"""
Initialization theta vector
:param X:
:param Y:
:return: Theta vector + Matrix for any class
"""
# Split X:
X1 = Matrix.from_list([X[i] for i in range(X.numrows) if Y[i][0] == 1])
X2 = Matrix.from_list([X[i] for i in range(X.numrows) if Y[i][0] == 0])
# A and B are avg data point of X1 and X2:
A = [sum([X1[i][1] for i in range(X1.numrows)])/X1.numrows, sum([X1[i][2] for i in range(X1.numrows)])/X1.numrows]
B = [sum([X2[i][1] for i in range(X2.numrows)])/X2.numrows, sum([X2[i][2] for i in range(X2.numrows)])/X2.numrows]
# Vector AB:
AB = [B[0] - A[0], B[1] - A[1]]
# I is midpoint between A and B:
I = [(A[0] + B[0])/2, (A[1] + B[1])/2]
# Get equation of AB's bisection:
theta1 = np.array([[-(AB[0]*I[0] + AB[1]*I[1])], [AB[0]], [AB[1]]])
# Scale theta:
div = theta1[0][0]
for i in range(len(theta1)):
if abs(theta1[i][0]) > abs(div):
div = theta1[i][0]
for i in range(len(theta1)):
theta1[i][0] /= div
# Return theta and matrix of any class
return [Matrix.from_list(theta1), X1, X2]
def test(self, m, size):
one = Matrix(size)
one.one()
e_lu = (m * m.invertLU() - one).frobeniusNorm()
e_qr_simple = (m * m.invertQRSimple() - one).frobeniusNorm()
e_qr_house = (m * m.invertQRHouseholder() - one).frobeniusNorm()
self.putMany([size, e_lu, e_qr_simple, e_qr_house])
def testKnown(self):
m = Matrix([[1,2,3,4], [1,4,9,16], [1,8,27,64], [1,16,81,256]])
l = Matrix([[1,0,0,0], [1,1,0,0], [1,3,1,0], [1,7,6,1]])
u = Matrix([[1,2,3,4], [0,2,6,12], [0,0,6,24], [0 ,0,0,24]])
(x,y) = m.lu()
self.assertTrue(x.isAlmostEqual(l))
self.assertTrue(y.isAlmostEqual(u))
self.assertTrue(m.isAlmostEqual(x*y))
def creat_matrix(self, de, boomnum):
m = Matrix()
temp = []
for i in range(de**2):
temp.append(0)
for i in range(boomnum):
index = random.randint(0, de**2 - 1)
temp[index] = 1
m.square(de, temp)
self.checkerboard = m.copy
def __init__(self, N):
self.shadow = None
self.checkerboard = None
self.boomlist = []
m = Matrix()
if N == 'easy':
m.full(8, 8, u'■')
self.print = m.copy
self.creat_matrix(8, 10)
self.run()
def get_shadow(self, board):
self.shadow = Matrix()
self.shadow.square(len(board) + 2, full=0)
for i in range(len(board)):
for j in range(len(board)):
if board.matrix[i][j] != 0:
for k in range(3):
self.shadow.matrix[i][j + k] += 1
self.shadow.matrix[i + 1][j + k] += 1
self.shadow.matrix[i + 2][j + k] += 1
def testRandom(self):
l = Matrix(self.size)
u = Matrix(self.size)
l.randomLowerTriangular(5)
u.randomUpperTriangular(5)
m = l * u
one = Matrix(self.size)
one.one()
self.assertTrue(one.isAlmostEqual(m * m.invertLU()))
def open_csv(path, header=None):
matrix = Matrix()
if header is None:
pds = read_csv(path, header=None)
else:
pds = read_csv(path, header=header)
temp = []
for i in range(len(pds)):
temp.append(pds.loc[i])
matrix.get_matrix(temp)
return matrix
def get_theta(m, x1, x2, y):
t = Matrix(m, 3, fill=1)
for i in range(0, m):
t[i][1] = x1[i]
t[i][2] = x2[i]
k = Matrix(m, 1, fill=0)
for i in range(0, m):
k[i][0] = y[i]
return ((t.trans() * t).inv()) * t.trans() * k
def __init__(self, key: list = None):
if not key:
key = Matrix.identity(3)
self.key_matrix = Matrix.from_list(key)
try:
determinant_modulus_inverse = pow(round(self.key_matrix.det()), -1,
26)
self.key_matrix_inverse = (determinant_modulus_inverse *
self.key_matrix.adjoint()) % 26
except:
raise Exception("Key matrix have to be modular inversible")
def test(self, m, size):
one = Matrix(size)
one.one()
m.random(int(time()))
q, r = m.QRSimple()
a_simple = (q*q.transpose() - one).frobeniusNorm()
b_simple = (m - q*r).frobeniusNorm()
q, r = m.QRHouseholder()
a_house = (q*q.transpose() - one).frobeniusNorm()
b_house = (m - q*r).frobeniusNorm()
self.putMany([size, a_simple, b_simple, a_house, b_house])
def testRandom(self): m = Matrix(self.size) m.random(3) q, r = m.QRHouseholder() one = Matrix(self.size) one.one() self.assertTrue(m.isAlmostEqual(q*r)) self.assertTrue(one.isAlmostEqual(q*q.transpose()))
def testRandom(self): m = Matrix(5) m.random(3) q, r = m.QRSimple() one = Matrix(5) one.one() self.assertTrue(m.isAlmostEqual(q*r)) self.assertTrue(one.isAlmostEqual(q*q.transpose()))
def get_matrix(data_input):
"""
Init X and Y matrix
:param data_input: input file path
:return: Matrix X and Y
"""
# Read data to dataframe df:
df = (pd.read_csv(data_input)).values
# Get X and Y:
X = Matrix.from_list([df[i][0:2] for i in range(len(df))])
Y = Matrix.from_list([df[i][2:] for i in range(len(df))])
# Append 1 into any row of X:
X = Matrix.from_list([np.insert(X[i], 0, (1,)) for i in range(len(df))])
# Return:
return [X, Y]
def instanMatriz():
global matrices, matrizActual, nombreActual
#print(matrizActual)
print(matrizActual)
matriz = Matrix(matrizActual)
#print(matriz)
matrices[nombreActual] = matriz
def sigmoid_function(theta, X):
"""
Cacl sigmoid value
:param theta:
:param X:
:return:
"""
z = Matrix.from_list(X) * (-theta)
return 1.0 / (1 + np.exp(z[0][0]))
def get_matrix(var):
"""
Lay ma tran theta, X va Y
:param var: Ma tran tham so dau vao
:return: list 3 ma tran: theta, X va Y
"""
# Y la cot cuoi cung cua var, con lai thuoc X
X = [var[i] for i in range(len(var) - 1)]
Y = var[len(var) - 1]
# Them value 1 vao dau moi dong cua X
X.insert(0, [1 for i in range(len(var[0]))])
# Tao ma tran X va Y:
mX = Matrix.from_list(X)
mY = Matrix.from_list([Y])
# Tra ve theta, X va Y:
return [((mX * mX.trans()).inv()) * mX * mY.trans(), mX, mY]
def testUpperTriangularInversion(self):
a = Matrix(15)
a.randomUpperTriangular(87)
b = a.invertUpperTriangular()
c = Matrix(15)
c.one()
print (c - a * b).frobeniusNorm()
self.assertTrue(c.isAlmostEqual(a * b))
self.assertTrue(c.isAlmostEqual(b * a))
def update_equation(X, Y, theta, alpha):
"""
Update Decision Boundary
:param X:
:param Y:
:param theta:
:param alpha:
:return: New theta vector
"""
return theta - Matrix.from_list([[alpha * cost_function(X, Y, theta)] for i in range(theta.numrows)])
def one(self):
res = NumMatrix()
one = Matrix()
empty = Matrix()
res.full(12, 4, 0)
one.full(12, 2, 1)
empty.full(12, 1, 0)
res.__putr(one)
res.__putr(empty)
for i in range(len(res.matrix)):
for j in range(len(res.matrix[0])):
if res.matrix[i][j] == 0:
res.matrix[i][j] = ' '
self.matrix = res.matrix
def encrypt(self, plaintext: str) -> str:
"""
Encrypt a given string, returning an encrypted string.
Overrides StringCipher.encrypt()
"""
no_space_plaintext = plaintext.replace(" ", "")
if not no_space_plaintext.isalpha():
raise Exception(
"Hill cipher only accept alphabets with or without spaces as plaintext"
)
lowercase_plaintext = no_space_plaintext.lower()
plaintext_mod_26_list = [
ord(char) - ord('a') for char in lowercase_plaintext
]
ciphertext_mod_26_list = []
block = self.key_matrix.numcols
offset = 0
while offset < len(plaintext_mod_26_list):
plaintext_slice = None
if len(plaintext_mod_26_list) - offset < block:
padding = [
0
for x in range(len(plaintext_mod_26_list), offset + block)
]
plaintext_slice = plaintext_mod_26_list[
offset:len(plaintext_mod_26_list)] + padding
else:
plaintext_slice = plaintext_mod_26_list[offset:offset + block]
plaintext_matrix_block = Matrix.from_list([[x]
for x in plaintext_slice
])
ciphertext_matrix_block = self.key_matrix * plaintext_matrix_block
for x in ciphertext_matrix_block.elements():
ciphertext_mod_26_list.append(x % 26)
offset += block
ciphertext = ''.join(
[chr(x + ord('a')) for x in ciphertext_mod_26_list]).upper()
return ciphertext
def testRandom(self): l = Matrix(5) l.randomLowerTriangular(5) u = Matrix(5) u.randomUpperTriangular(5) m = l*u (x,y) = m.lu() self.assertTrue(m.isAlmostEqual(x*y))
def infer(self, data_in):
"""Push data_in through network and give results."""
l3 = Matrix.from_list([data_in]) * self.l1 + self.b1
# l3 = NeuralNetwork._logistic(l3)
l3 = l3 * self.l2 + self.b2
# l3 = NeuralNetwork._logistic(l3)
e = [math.exp(elem) for elem in l3[0]]
s = sum(e)
odds = [elem / s for elem in e]
return self.argmax(odds)
def seven(self):
res = NumMatrix()
res.full(2, 6, 7)
one = NumMatrix()
one.full(10, 4, 0)
two = Matrix()
two.full(10, 2, 7)
one.__putr(two)
empty = Matrix()
empty.full(12, 1, 0)
res.__putd(one)
res.__putr(empty)
for i in range(len(res.matrix)):
for j in range(len(res.matrix[0])):
if res.matrix[i][j] == 0:
res.matrix[i][j] = ' '
self.matrix = res.matrix
def cost_function(X, Y, theta):
"""
Cacl avg cost value
:param X:
:param Y:
:param theta:
:return: Cost value
"""
m = X.numrows # Data length
# Cacl total cost:
cost = ([(-Y[i][0] * np.log(sigmoid_function(theta, [X[i]]))
- (1 - Y[i][0]) * np.log(1 - sigmoid_function(theta, [X[i]])))
* Matrix.from_list([[X[i][0]]]) for i in range(m)])
# Return avg cost:
return -sum([cost[i][0][0] for i in range(len(cost))])/m
def decrypt(self, ciphertext: str) -> str:
"""
Decrypt a given string, returning a plaintext string.
Overrides StringCipher.decrypt()
"""
if not ciphertext.isalpha():
raise Exception("Hill cipher only accept alphabets as ciphertext")
lowercase_ciphertext = ciphertext.lower()
ciphertext_mod_26_list = [
ord(char) - ord('a') for char in lowercase_ciphertext
]
plaintext_mod_26_list = []
block = self.key_matrix.numcols
offset = 0
while offset < len(ciphertext_mod_26_list):
ciphertext_slice = None
if len(ciphertext_mod_26_list) - offset < block:
padding = [
0
for x in range(len(ciphertext_mod_26_list), offset + block)
]
ciphertext_slice = ciphertext_mod_26_list[
offset:len(ciphertext_mod_26_list)] + padding
else:
ciphertext_slice = ciphertext_mod_26_list[offset:offset +
block]
ciphertext_matrix_block = Matrix.from_list(
[[x] for x in ciphertext_slice])
plaintext_matrix_block = self.key_matrix_inverse * ciphertext_matrix_block
for x in plaintext_matrix_block.elements():
plaintext_mod_26_list.append(round(x) % 26)
offset += block
plaintext = ''.join([chr(x + ord('a')) for x in plaintext_mod_26_list])
return plaintext
def love(num, printer=None):
lover = MagicMatrix()
op = assistfuction.Functions()
one = Spmatrix()
one.uper_ting(3, model=3)
two = Matrix([[1], [1], [1]])
three = Matrix([[0, 0, 0], [1, 0, 1], [1, 1, 1]])
five = Spmatrix()
five.uper_ting(3, model=2)
five.T
lover.get_matrix(op.split_matrix(one))
lover.__putr(two)
lover.__putr(three)
lover.__putr(two)
lover.__putr(five)
add = Matrix()
add.full(num, 11, 1)
lover.__putd(add)
six = Spmatrix()
six.uper_ting(5, model=2)
seven = Matrix([[1], [1], [1], [1], [1]])
eight = Spmatrix()
eight.uper_ting(5, model=1)
eight.T
temp2 = MagicMatrix()
temp2.get_matrix(op.split_matrix(six))
temp2.__putr(seven)
temp2.__putr(eight)
lover.__putd(temp2)
end = Matrix([[0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]])
lover.__putd(end)
for i in range(len(lover.matrix)):
for j in range(len(lover.matrix[0])):
if lover.matrix[i][j] == 0:
lover.matrix[i][j] = ' '
else:
lover.matrix[i][j] = u'♥'
if printer is not None:
print('\033[31m', lover, '\033[0m')
return lover.copy
def update_equation(X, Y, theta, alpha, _lambda):
"""
Update Decision Boundary
:param X:
:param Y:
:param theta:
:param alpha:
:return: New theta vector
"""
temp = theta[0][0] # Save theta0 value
# Update theta(included theta0):
theta = theta - Matrix.from_list(
[[alpha * cost_function(X, Y, theta, _lambda)]
for i in range(theta.numrows)])
# Update theta0 by special way:
theta[0][0] = temp - alpha * cost_function(X, Y, theta, 0)
return theta
def zero(self):
res = NumMatrix()
res.full(2, 6, 1)
one = NumMatrix()
one.full(8, 2, 1)
two = Matrix()
two.full(8, 2, 0)
three = one.copy
one.__putr(two)
one.__putr(three)
four = res.copy
res.__putd(one)
res.__putd(four)
empty = Matrix()
empty.full(12, 1, 0)
res.__putr(empty)
for i in range(len(res.matrix)):
for j in range(len(res.matrix[0])):
if res.matrix[i][j] == 0:
res.matrix[i][j] = ' '
else:
res.matrix[i][j] = 0
self.matrix = res.matrix
def genMatrix(size, kind):
m = Matrix(size)
if kind == "random":
m.random(seed())
elif kind == "triangular":
m.randomUpperTriangular(seed())
elif kind == "hilbert":
m.hilbert()
elif kind == "diagonal_dominant":
m.randomDiagonalDominant(seed())
elif kind == "almost_singular":
m.randomAlmostSingular(seed())
elif kind == "zero_minor":
m.randomZeroMinor(seed())
elif kind == "tridiagonal":
m.randomTridiagonal(seed())
else:
return None
return m
def testManipulators(self): a = Matrix(44) a.zero() a.one() a.hilbert()
from pymatrix import Matrix
import pandas as pd
import numpy as np
from Regularization import Hx, get_matrix
if __name__ == '__main__':
df1 = pd.read_csv("result.txt").values
df2 = (pd.read_csv("ex2data2.txt")).values
temp = get_matrix("ex2data2.txt")
X = temp[0]
Y = temp[1]
theta = Matrix.from_list(df1)
for i in range(len(df1)):
np.append(df1[i], 1 - Hx(theta, X, 6))
print df1
def testRandom(self): m = Matrix(self.size) m.random(3); one = Matrix(self.size) one.one() self.assertTrue(one.isAlmostEqual(m * m.invertQRHouseholder()))
def testClone(self): m = Matrix(7) a = m.copy() self.assertTrue(m.isAlmostEqual(a))
def _logistic(x):
r = Matrix(1, 2)
for row, col, elem in x.elements():
r[row][col] = 1.0 / (1 + math.exp(-elem))
return r
from pymatrix import Matrix from time import time minsize = 10 maxsize = 200 step = 5 print "# time test" print "# size LU QR-Gram QR-Householder" for size in range(minsize, maxsize + 1, step): m = Matrix(size) m.random(int(time())) m, lu_time = m.invertLUTimed() m, qr_gram_time = m.invertQRSimpleTimed() m, qr_house_time = m.invertQRHouseholderTimed() print size, lu_time, qr_gram_time, qr_house_time
import numpy as np
from pymatrix import Matrix
from pymatrix import Spmatrix
from pymatrix import Operation
import random
class FeatureMatrix(Matrix):
@staticmethod
def create_rotation_matrix(dimension, p, q, theta, rou=3):
matrix_origin = Spmatrix.diagonal(Spmatrix(), dimension, 1)
matrix_origin.matrix[p][p] = round(np.cos(theta), rou)
matrix_origin.matrix[p][q] = round(-np.sin(theta), rou)
matrix_origin.matrix[q][p] = round(np.sin(theta), rou)
matrix_origin.matrix[q][q] = round(np.cos(theta), rou)
return matrix_origin
if __name__ == '__main__':
l = []
for i in range(5):
l.append([])
for j in range(5):
l[i].append(random.randint(1, 10))
m = Matrix(l)
f = FeatureMatrix.create_rotation_matrix(5, 2, 1, 1.047)
print(m)
print(f)
print(Operation.mul(m, f))
class Game:
def __init__(self, N):
self.shadow = None
self.checkerboard = None
self.boomlist = []
m = Matrix()
if N == 'easy':
m.full(8, 8, u'■')
self.print = m.copy
self.creat_matrix(8, 10)
self.run()
def init(self):
print(self.print)
def limit(self, x, y):
if x < 0 or x > 7 or y < 0 or y > 7:
return False
else:
return True
def check(self):
index = []
for i in range(8):
for j in range(8):
if self.print.matrix[i][j] == ' ':
index.append([i, j])
for x, y in index:
if x + 1 < 7:
if self.shadow.matrix[x + 2][y + 1] != 0:
self.print.matrix[x + 1][y] = self.shadow.matrix[x + 2][y +
1]
if x - 1 > 0:
if self.shadow.matrix[x][y + 1] != 0:
self.print.matrix[x - 1][y] = self.shadow.matrix[x][y + 1]
if y - 1 > 0:
if self.shadow.matrix[x + 1][y] != 0:
self.print.matrix[x][y - 1] = self.shadow.matrix[x + 1][y]
if y + 1 < 7:
if self.shadow.matrix[x + 1][y + 2] != 0:
self.print.matrix[x][y + 1] = self.shadow.matrix[x + 1][y +
2]
def get_shadow(self, board):
self.shadow = Matrix()
self.shadow.square(len(board) + 2, full=0)
for i in range(len(board)):
for j in range(len(board)):
if board.matrix[i][j] != 0:
for k in range(3):
self.shadow.matrix[i][j + k] += 1
self.shadow.matrix[i + 1][j + k] += 1
self.shadow.matrix[i + 2][j + k] += 1
def reflash(self, point, MODEL=None):
x, y = point
queue = [point]
if self.checkerboard.matrix[x][
y] == 0 and MODEL is None and self.shadow.matrix[x +
1][y +
1] == 0:
self.print.matrix[x][y] = ' '
self.init()
while queue:
x, y = queue.pop(0)
if self.limit(x + 1, y) and self.print.matrix[
x + 1][y] == u'■' and self.shadow.matrix[x +
2][y +
1] == 0:
queue.append((x + 1, y))
self.print.matrix[x + 1][y] = ' ' if self.shadow.matrix[
x + 2][y + 1] == 0 else self.shadow.matrix[x + 2][y +
1]
if self.limit(x - 1, y) and self.print.matrix[
x - 1][y] == u'■' and self.shadow.matrix[x][y +
1] == 0:
queue.append((x - 1, y))
self.print.matrix[x - 1][y] = ' ' if self.shadow.matrix[x][
y + 1] == 0 else self.shadow.matrix[x][y + 1]
if self.limit(x, y + 1) and self.print.matrix[x][
y + 1] == u'■' and self.shadow.matrix[x + 1][y +
2] == 0:
queue.append((x, y + 1))
self.print.matrix[x][y + 1] = ' ' if self.shadow.matrix[
x + 1][y + 2] == 0 else self.shadow.matrix[x + 1][y +
2]
if self.limit(x, y - 1) and self.print.matrix[x][
y - 1] == u'■' and self.shadow.matrix[x + 1][y] == 0:
queue.append((x, y - 1))
self.print.matrix[x][y - 1] = ' ' if self.shadow.matrix[
x + 1][y] == 0 else self.shadow.matrix[x + 1][y]
self.check()
self.init()
elif MODEL == 'over':
self.print.matrix[x][y] = u'♂'
self.init()
else:
self.print.matrix[x][y] = self.shadow.matrix[x + 1][y + 1]
self.init()
def draw(self, point, MODEL=None):
return self.reflash(point, MODEL=MODEL)
def judge(self, point):
if point in self.boomlist:
print('Game over!')
self.draw(point, 'over')
return False
else:
return True
def win(self):
num = 0
for i in self.checkerboard:
if i != 0:
num += 1
for i in self.print:
if i == u'■':
num -= 1
if num == 0:
return True
else:
return False
def creat_matrix(self, de, boomnum):
m = Matrix()
temp = []
for i in range(de**2):
temp.append(0)
for i in range(boomnum):
index = random.randint(0, de**2 - 1)
temp[index] = 1
m.square(de, temp)
self.checkerboard = m.copy
def run(self):
self.init()
get_piont_r, get_piont_l = input().split(',')
get_piont_r, get_piont_l = int(get_piont_r), int(get_piont_l)
point = (get_piont_r, get_piont_l)
if self.checkerboard.matrix[get_piont_r][get_piont_l] != 0:
self.checkerboard.matrix[get_piont_r][get_piont_l] = 0
self.get_shadow(self.checkerboard)
for i in range(len(self.checkerboard.matrix)):
for j in range(len(self.checkerboard.matrix[0])):
if self.checkerboard.matrix[i][j] != 0:
self.boomlist.append((i, j))
while self.judge((get_piont_r, get_piont_l)):
if self.win():
print('YOU WIN!')
break
self.reflash(point)
get_piont_r, get_piont_l = input().split(',')
get_piont_r, get_piont_l = int(get_piont_r), int(get_piont_l)
point = (get_piont_r, get_piont_l)
def testUnit(self): one = Matrix(self.size) one.one() self.assertTrue(one.isAlmostEqual(one.invertLU()))
def testHilbert(self): m = Matrix(50) m.hilbert() (l,u) = m.lu() self.assertTrue(m.isAlmostEqual(l*u))
def test_from_matrix(self):
assert P(Matrix.from_list([[0.4], [0.2], [0.1]])) == P([0.4, 0.2, 0.1])
def testTransposition(self): m = Matrix([[1,2],[3,4]]) tr = Matrix([[1,3], [2,4]]) self.assertTrue(tr.isAlmostEqual(m.transpose()))
def testNorms(self): a = Matrix(44) a.frobeniusNorm() a.scaledFrobeniusNorm() a.firstNorm() a.infNorm()
num += 1
class Body(tk.Frame):
def __init__(self, master):
super().__init__(master)
self.master = master
self.master.place(relx=0.45, rely=0.05)
background_image = tk.PhotoImage(
file=r'C:\Python3\Lib\site-packages\pymatrix\head.gif')
self.widget = tk.Label(master, image=background_image)
self.widget.pack()
if __name__ == '__main__':
m = Matrix([[98 * 5, 98 * (-2), 0], [-2 * 98, 3 * 98, -98], [0, -98, 98]])
oper = Operation()
mr = Spmatrix().diagonal(3, 0.27)
a = oper.mul(oper.inverse(mr), m)
for i in range(len(a)):
for j in range(len(a)):
a.matrix[i][j] = round(a.matrix[i][j], 2)
print('原始矩阵为：', a)
find = oper.feature_matrix(a, 3, e=0.1)
# print(oper.feature_matrix(m, 3, e=0.1))
# print('特征矩阵：', find['eigenvector'])
print('特征值', find['eigenvalues'])
# vector = [[3, 4, 5]]
# s = Spmatrix()
# print(isinstance(s, Matrix))
def colvec(l):
return Matrix.from_list([l]).trans()
def __init__(self):
self.l1 = Matrix(16, 16)
self.b1 = Matrix(1, 16)
self.l2 = Matrix(16, 4)
self.b2 = Matrix(1, 4)
comando = StringVar()
E = Entry(
root,
relief=FLAT,
highlightbackground="black",
textvariable=comando,
width=79,
bd=0,
bg="black",
fg="white",
insertbackground="white",
)
E.place(x=75, y=415)
matrices = {
"a": Matrix([[3, 2, 1], [1, 1, 3], [0, 2, 1]]),
"b": Matrix([[2, 1], [1, 0], [3, 2]])
}
state = "idle"
contMat = 2
nombreActual = ""
matrizActual = []
def insertarTexto(texto, start="\n"):
global text
text.config(state=NORMAL)
text.insert(END, "{0}{1}".format(start, texto))
text.see(END)
text.config(state=DISABLED)
def testColumnNormalization(self): m = Matrix([[3,3],[4,4]]) norm = Matrix([[0.6, 0.6],[0.8,0.8]]) m.normalizeColumn(0) m.normalizeColumn(1) self.assertTrue(norm.isAlmostEqual(m))
