2021-05-31 18:06:14 +02:00
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from copy import deepcopy
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from matplotlib import pyplot as plt
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2021-05-26 20:43:17 +02:00
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from typing import Tuple
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2021-05-31 18:06:14 +02:00
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2021-05-26 20:43:17 +02:00
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import numpy as np
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2021-05-31 18:06:14 +02:00
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from scipy.linalg import solve
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2021-05-26 20:43:17 +02:00
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2021-05-26 21:00:08 +02:00
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"""
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Class that performs qr decomposition. Use methods:
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perform_householder_QR,
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perform_givens_QR
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2021-05-31 18:06:14 +02:00
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both accept np.ndarray that fulfils m >= n condition is 2d, and contains real numbers.
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2021-05-26 21:00:08 +02:00
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"""
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2021-05-31 18:06:14 +02:00
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2021-05-26 20:43:17 +02:00
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class QR:
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def __init__(self) -> None:
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pass
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2021-05-26 20:43:17 +02:00
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"""
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2021-05-31 18:06:14 +02:00
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Checks if calculated matrices fulfill QR decomposition conditions, that is:
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2021-05-26 20:43:17 +02:00
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A = QR , where Q -> Q * Qt = I and R is a upper triangular matrix
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"""
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2021-05-31 18:06:14 +02:00
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2021-05-26 20:43:17 +02:00
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def __check_condition(self, Q: np.matrix, R: np.matrix) -> bool:
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if not np.allclose(R, np.triu(R)):
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print("R matrix is not upper triangle.")
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return False
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I = np.identity(Q.shape[1])
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comparison = np.equal(np.matmul(np.transpose(Q), Q), I)
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if not comparison.all():
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print("Q matrix is not orthogonal.")
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return False
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return True
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2021-05-31 18:06:14 +02:00
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2021-05-26 20:43:17 +02:00
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"""
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Checks if given matrix is 2d, m >= n and filled with real numbers
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"""
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def __check_pre_conditions(self, matrix: np.ndarray) -> bool:
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if not matrix.shape[0] >= matrix.shape[1]:
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print("Matrix is m is lesser than n.")
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return False
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if len(matrix.shape) != 2:
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print("Matrix is not 2D.")
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return False
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if not np.isreal(matrix).all():
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print("Matrix doesn't contain all real numbers.")
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return False
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return True
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"""
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2021-05-31 18:06:14 +02:00
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Method that performs Householder Transformation QR, accepts 2D, real numbers matrix, that
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2021-05-26 20:43:17 +02:00
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fulfills m >= n condition. Return Q and R matrices.
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"""
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def perform_householder_QR(self, matrix: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
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if self.__check_pre_conditions(matrix):
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return self.__householder_qr(matrix)
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else:
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print("Incorrect type.")
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raise Exception
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def __householder(self, matrix: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
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v = matrix / (matrix[0] + np.copysign(np.linalg.norm(matrix), matrix[0]))
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v[0] = 1
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tau = 2 / (v.T @ v)
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return v, tau
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def __householder_qr(self, matrix: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
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m, n = matrix.shape
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R = matrix.copy()
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Q = np.identity(m)
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for j in range(0, n):
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v, tau = self.__householder(R[j:, j, np.newaxis])
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H = np.identity(m)
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H[j:, j:] -= tau * (v @ v.T)
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R = H @ R
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Q = H @ Q
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return Q[:n].T, np.triu(R[:n])
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def __givens_qr(self, matrix: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
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m, n = matrix.shape
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R = matrix
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Q = np.eye(m)
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G = np.zeros((2, 2))
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for j in range(n):
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for i in reversed(range(j + 1, m)):
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a, b = R[i - 1, j], R[i, j]
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G = np.asarray([[a, b], [-b, a]]) / np.sqrt(a ** 2 + b ** 2)
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R[i - 1:i + 1, j] = G @ R[i - 1:i + 1, j]
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Q[i - 1:i + 1, :] = G @ Q[i - 1:i + 1, :]
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return Q.T, R
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"""
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2021-05-31 18:06:14 +02:00
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Method that performs Givens Rotation QR, accepts 2D, real numbers matrix, that
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2021-05-26 20:43:17 +02:00
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fulfills m >= n condition. Return Q and R matrices.
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"""
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def perform_givens_QR(self, matrix: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
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if self.__check_pre_conditions(matrix):
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return self.__givens_qr(matrix)
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else:
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print("Incorrect type.")
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raise Exception
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2021-05-31 23:16:20 +02:00
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def solve_least_squares(self, A: np.ndarray, b: np.array, is_givens):
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if is_givens:
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Q, R = self.perform_givens_QR(A)
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else:
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Q, R = self.perform_householder_QR(A)
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x = solve(R, np.dot(Q.T, b))
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return x
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def __design_matrix(self, A: np.ndarray):
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return np.hstack((np.ones(A.shape[0]).reshape(-1, 1), A[:, :-1]))
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def fit_poly(self, A: np.ndarray, is_givens: bool):
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return self.solve_least_squares(
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np.dot(self.__design_matrix(A), self.__design_matrix(A).T),
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A[:, -1:].reshape(-1, 1),
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is_givens=is_givens)
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"""
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Usage example
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"""
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if __name__ == "__main__":
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qr = QR()
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matrix = np.matrix('0 0 0; 1 1 2; 1 2 4; 3 3 5; 5 6 7; 8 9 10')
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matrix = np.asarray(matrix)
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print(matrix)
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a = deepcopy(matrix)
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b = deepcopy(matrix)
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c = deepcopy(matrix)
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Q, R = qr.perform_householder_QR(a)
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print("Householder:")
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print(Q)
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print(R)
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print("Givens")
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Q, R = qr.perform_givens_QR(b)
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print(Q)
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print(R)
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# dla porównania
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print("Numpy")
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Q, R = np.linalg.qr(c)
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print(Q)
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print(R)
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print('solve least squares')
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2021-06-01 07:48:16 +02:00
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print(matrix[:2, :-1])
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print(matrix[:2, -1:])
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print(qr.solve_least_squares(matrix[:2, :-1], matrix[:2, -1:], is_givens=True))
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2021-06-01 14:48:18 +02:00
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def PolyCoefficients(x1,x2, coeffs):
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""" Returns a polynomial for ``x`` values for the ``coeffs`` provided.
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The coefficients must be in ascending order (``x**0`` to ``x**o``).
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"""
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o = len(coeffs)
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y = 0
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for i in range(o):
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y += coeffs[i] * x1 ** i + coeffs[i] * x2 ** i
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return y
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def f(a, b):
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return a + 2 * (b ** 2)
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x1 = np.asarray(np.random.randint(0,10,5))
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x2 = np.asarray(np.random.randint(0,10,5))
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y = f(x1, x2)
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2021-06-01 08:01:23 +02:00
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mat = np.asarray([x1, x2, y])
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plt3d = plt.figure().gca(projection='3d')
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#xx,yy = np.meshgrid(np.linspace(0,10,11),np.linspace(0,10,11))
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xx, yy = np.meshgrid(range(10), range(10))
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plt3d.plot_surface(xx, yy, f(xx, yy), alpha=0.2)
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plt3d.plot_surface(xx, yy, PolyCoefficients(xx,yy, qr.fit_poly(mat.T, is_givens=True)), alpha=0.2)
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plt.show()
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print(qr.fit_poly(mat.T, is_givens=True))
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