2021-10-12 13:57:41 +01:00
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import sys
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sys.path.append('..')
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import autograd.numpy as np
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import os
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import random
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import math
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import string
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import copy
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import scipy.optimize
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from scipy.odr import ODR, Model, Data, RealData
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import pyerrors as pe
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import pytest
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2021-10-15 13:05:00 +01:00
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np.random.seed(0)
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2021-10-12 13:57:41 +01:00
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def test_matrix_functions():
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dim = 3 + int(4 * np.random.rand())
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print(dim)
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matrix = []
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for i in range(dim):
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row = []
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for j in range(dim):
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row.append(pe.pseudo_Obs(np.random.rand(), 0.2 + 0.1 * np.random.rand(), 'e1'))
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matrix.append(row)
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matrix = np.array(matrix) @ np.identity(dim)
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# Check inverse of matrix
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inv = pe.linalg.mat_mat_op(np.linalg.inv, matrix)
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check_inv = matrix @ inv
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for (i, j), entry in np.ndenumerate(check_inv):
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entry.gamma_method()
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if(i == j):
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assert math.isclose(entry.value, 1.0, abs_tol=1e-9), 'value ' + str(i) + ',' + str(j) + ' ' + str(entry.value)
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else:
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assert math.isclose(entry.value, 0.0, abs_tol=1e-9), 'value ' + str(i) + ',' + str(j) + ' ' + str(entry.value)
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assert math.isclose(entry.dvalue, 0.0, abs_tol=1e-9), 'dvalue ' + str(i) + ',' + str(j) + ' ' + str(entry.dvalue)
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# Check Cholesky decomposition
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sym = np.dot(matrix, matrix.T)
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cholesky = pe.linalg.mat_mat_op(np.linalg.cholesky, sym)
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check = cholesky @ cholesky.T
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for (i, j), entry in np.ndenumerate(check):
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diff = entry - sym[i, j]
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diff.gamma_method()
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assert math.isclose(diff.value, 0.0, abs_tol=1e-9), 'value ' + str(i) + ',' + str(j)
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assert math.isclose(diff.dvalue, 0.0, abs_tol=1e-9), 'dvalue ' + str(i) + ',' + str(j)
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# Check eigh
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e, v = pe.linalg.eigh(sym)
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for i in range(dim):
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tmp = sym @ v[:, i] - v[:, i] * e[i]
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for j in range(dim):
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tmp[j].gamma_method()
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assert math.isclose(tmp[j].value, 0.0, abs_tol=1e-9), 'value ' + str(i) + ',' + str(j)
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assert math.isclose(tmp[j].dvalue, 0.0, abs_tol=1e-9), 'dvalue ' + str(i) + ',' + str(j)
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