Merge pull request #3 from HideyoshiNakazone/develop
Adds Setup.py and Implements Process Submodule in Cython
This commit is contained in:
GitHub
9
.gitignore
vendored
9
.gitignore
vendored
@@ -1,2 +1,9 @@
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*.pyc
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build/
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.vscode
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.idea/
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*.pyc
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*.so
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*.c
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yoshi_seals.egg-info/
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2
Pipfile
2
Pipfile
@@ -6,8 +6,10 @@ name = "pypi"
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[packages]
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numpy = "*"
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pandas = "*"
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cython = "*"
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[dev-packages]
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coverage = "*"
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[requires]
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python_version = "3.10"
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229
Pipfile.lock
generated
229
Pipfile.lock
generated
@@ -1,7 +1,7 @@
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"pipfile-spec": 6,
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"requires": {
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@@ -16,72 +16,118 @@
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|
||||
"sha256:9608000a5a45f663be6af5c70c3cbe634fa19243e720eb380c0d378666bc7702",
|
||||
"sha256:a40dd1e9f22e01e66ed534d6a965eb99546b41d4d52dbdb66565608fde48203f",
|
||||
"sha256:b4f5a82afa4f1ff482ab8ded2ae8a453a2cdfde2001567b3ca24a4c5c5ca0db3",
|
||||
"sha256:c009a92e81ce836212ce7aa98b219db7961a8b95999b97af566b8dc8c33e9519",
|
||||
"sha256:c218796d59d5abd8780170c937b812c9637e84c32f8271bbf9845970f8c1351f",
|
||||
"sha256:cc3cd122bea268998b79adebbb8343b735a5511ec14efb70a39e7acbc11ccbdc",
|
||||
"sha256:d0d8fd58df5d17ddb8c72a5075d87cd80d71b542571b5f78178fb067fa4e9c72",
|
||||
"sha256:e18bc3764cbb5e118be139b3b611bc3fbc5d3be42a7e827d1096f46087b395eb",
|
||||
"sha256:e2b83abd292194f350bb04e188f9379d36b8dfac24dd445d5c87575f3beaf789",
|
||||
"sha256:e7469271497960b6a781eaa930cba8af400dd59b62ec9ca2f4d31a19f2f91090",
|
||||
"sha256:e9dbacd22555c2d47f262ef96bb4e30880e5956169741400af8b306bbb24a273",
|
||||
"sha256:f6257b314fc14958f8122779e5a1557517b0f8e500cfb2bd53fa1f75a8ad0af2"
|
||||
],
|
||||
"index": "pypi",
|
||||
"version": "==1.5.0"
|
||||
"version": "==1.5.2"
|
||||
},
|
||||
"python-dateutil": {
|
||||
"hashes": [
|
||||
@@ -93,10 +139,10 @@
|
||||
},
|
||||
"pytz": {
|
||||
"hashes": [
|
||||
"sha256:220f481bdafa09c3955dfbdddb7b57780e9a94f5127e35456a48589b9e0c0197",
|
||||
"sha256:cea221417204f2d1a2aa03ddae3e867921971d0d76f14d87abb4414415bbdcf5"
|
||||
"sha256:222439474e9c98fced559f1709d89e6c9cbf8d79c794ff3eb9f8800064291427",
|
||||
"sha256:e89512406b793ca39f5971bc999cc538ce125c0e51c27941bef4568b460095e2"
|
||||
],
|
||||
"version": "==2022.2.1"
|
||||
"version": "==2022.6"
|
||||
},
|
||||
"six": {
|
||||
"hashes": [
|
||||
@@ -107,5 +153,62 @@
|
||||
"version": "==1.16.0"
|
||||
}
|
||||
},
|
||||
"develop": {}
|
||||
"develop": {
|
||||
"coverage": {
|
||||
"hashes": [
|
||||
"sha256:027018943386e7b942fa832372ebc120155fd970837489896099f5cfa2890f79",
|
||||
"sha256:11b990d520ea75e7ee8dcab5bc908072aaada194a794db9f6d7d5cfd19661e5a",
|
||||
"sha256:12adf310e4aafddc58afdb04d686795f33f4d7a6fa67a7a9d4ce7d6ae24d949f",
|
||||
"sha256:1431986dac3923c5945271f169f59c45b8802a114c8f548d611f2015133df77a",
|
||||
"sha256:1ef221513e6f68b69ee9e159506d583d31aa3567e0ae84eaad9d6ec1107dddaa",
|
||||
"sha256:20c8ac5386253717e5ccc827caad43ed66fea0efe255727b1053a8154d952398",
|
||||
"sha256:2198ea6fc548de52adc826f62cb18554caedfb1d26548c1b7c88d8f7faa8f6ba",
|
||||
"sha256:255758a1e3b61db372ec2736c8e2a1fdfaf563977eedbdf131de003ca5779b7d",
|
||||
"sha256:265de0fa6778d07de30bcf4d9dc471c3dc4314a23a3c6603d356a3c9abc2dfcf",
|
||||
"sha256:33a7da4376d5977fbf0a8ed91c4dffaaa8dbf0ddbf4c8eea500a2486d8bc4d7b",
|
||||
"sha256:42eafe6778551cf006a7c43153af1211c3aaab658d4d66fa5fcc021613d02518",
|
||||
"sha256:4433b90fae13f86fafff0b326453dd42fc9a639a0d9e4eec4d366436d1a41b6d",
|
||||
"sha256:4a5375e28c5191ac38cca59b38edd33ef4cc914732c916f2929029b4bfb50795",
|
||||
"sha256:4a8dbc1f0fbb2ae3de73eb0bdbb914180c7abfbf258e90b311dcd4f585d44bd2",
|
||||
"sha256:59f53f1dc5b656cafb1badd0feb428c1e7bc19b867479ff72f7a9dd9b479f10e",
|
||||
"sha256:5dbec3b9095749390c09ab7c89d314727f18800060d8d24e87f01fb9cfb40b32",
|
||||
"sha256:633713d70ad6bfc49b34ead4060531658dc6dfc9b3eb7d8a716d5873377ab745",
|
||||
"sha256:6b07130585d54fe8dff3d97b93b0e20290de974dc8177c320aeaf23459219c0b",
|
||||
"sha256:6c4459b3de97b75e3bd6b7d4b7f0db13f17f504f3d13e2a7c623786289dd670e",
|
||||
"sha256:6d4817234349a80dbf03640cec6109cd90cba068330703fa65ddf56b60223a6d",
|
||||
"sha256:723e8130d4ecc8f56e9a611e73b31219595baa3bb252d539206f7bbbab6ffc1f",
|
||||
"sha256:784f53ebc9f3fd0e2a3f6a78b2be1bd1f5575d7863e10c6e12504f240fd06660",
|
||||
"sha256:7b6be138d61e458e18d8e6ddcddd36dd96215edfe5f1168de0b1b32635839b62",
|
||||
"sha256:7ccf362abd726b0410bf8911c31fbf97f09f8f1061f8c1cf03dfc4b6372848f6",
|
||||
"sha256:83516205e254a0cb77d2d7bb3632ee019d93d9f4005de31dca0a8c3667d5bc04",
|
||||
"sha256:851cf4ff24062c6aec510a454b2584f6e998cada52d4cb58c5e233d07172e50c",
|
||||
"sha256:8f830ed581b45b82451a40faabb89c84e1a998124ee4212d440e9c6cf70083e5",
|
||||
"sha256:94e2565443291bd778421856bc975d351738963071e9b8839ca1fc08b42d4bef",
|
||||
"sha256:95203854f974e07af96358c0b261f1048d8e1083f2de9b1c565e1be4a3a48cfc",
|
||||
"sha256:97117225cdd992a9c2a5515db1f66b59db634f59d0679ca1fa3fe8da32749cae",
|
||||
"sha256:98e8a10b7a314f454d9eff4216a9a94d143a7ee65018dd12442e898ee2310578",
|
||||
"sha256:a1170fa54185845505fbfa672f1c1ab175446c887cce8212c44149581cf2d466",
|
||||
"sha256:a6b7d95969b8845250586f269e81e5dfdd8ff828ddeb8567a4a2eaa7313460c4",
|
||||
"sha256:a8fb6cf131ac4070c9c5a3e21de0f7dc5a0fbe8bc77c9456ced896c12fcdad91",
|
||||
"sha256:af4fffaffc4067232253715065e30c5a7ec6faac36f8fc8d6f64263b15f74db0",
|
||||
"sha256:b4a5be1748d538a710f87542f22c2cad22f80545a847ad91ce45e77417293eb4",
|
||||
"sha256:b5604380f3415ba69de87a289a2b56687faa4fe04dbee0754bfcae433489316b",
|
||||
"sha256:b9023e237f4c02ff739581ef35969c3739445fb059b060ca51771e69101efffe",
|
||||
"sha256:bc8ef5e043a2af066fa8cbfc6e708d58017024dc4345a1f9757b329a249f041b",
|
||||
"sha256:c4ed2820d919351f4167e52425e096af41bfabacb1857186c1ea32ff9983ed75",
|
||||
"sha256:cca4435eebea7962a52bdb216dec27215d0df64cf27fc1dd538415f5d2b9da6b",
|
||||
"sha256:d900bb429fdfd7f511f868cedd03a6bbb142f3f9118c09b99ef8dc9bf9643c3c",
|
||||
"sha256:d9ecf0829c6a62b9b573c7bb6d4dcd6ba8b6f80be9ba4fc7ed50bf4ac9aecd72",
|
||||
"sha256:dbdb91cd8c048c2b09eb17713b0c12a54fbd587d79adcebad543bc0cd9a3410b",
|
||||
"sha256:de3001a203182842a4630e7b8d1a2c7c07ec1b45d3084a83d5d227a3806f530f",
|
||||
"sha256:e07f4a4a9b41583d6eabec04f8b68076ab3cd44c20bd29332c6572dda36f372e",
|
||||
"sha256:ef8674b0ee8cc11e2d574e3e2998aea5df5ab242e012286824ea3c6970580e53",
|
||||
"sha256:f4f05d88d9a80ad3cac6244d36dd89a3c00abc16371769f1340101d3cb899fc3",
|
||||
"sha256:f642e90754ee3e06b0e7e51bce3379590e76b7f76b708e1a71ff043f87025c84",
|
||||
"sha256:fc2af30ed0d5ae0b1abdb4ebdce598eafd5b35397d4d75deb341a614d333d987"
|
||||
],
|
||||
"index": "pypi",
|
||||
"version": "==6.5.0"
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
20
makefile
Normal file
20
makefile
Normal file
@@ -0,0 +1,20 @@
|
||||
WORK=./yoshi_seals
|
||||
PROCESS=$(WORK)/process
|
||||
SHARED=$(WORK)/shared
|
||||
|
||||
SETUP_CYTHON=setup_cython.py
|
||||
|
||||
OBJS= $(PROCESS)/process.pyx $(SHARED)/array.pyx $(SETUP_CYTHON)
|
||||
|
||||
PYTHON?=python
|
||||
PARALLEL?=$(shell ${PYTHON} -c 'import sys; print("-j5" if sys.version_info >= (3,5) else "")' || true)
|
||||
|
||||
all: $(OBJS)
|
||||
python3 $(SETUP_CYTHON) build_ext --inplace $(PARALLEL) && \
|
||||
rm -r build
|
||||
|
||||
clean:
|
||||
|
||||
rm $(WORK)/**/*.cpython-310-x86_64-linux-gnu.so
|
||||
rm $(WORK)/**/*.c
|
||||
|
||||
35
setup.py
Normal file
35
setup.py
Normal file
@@ -0,0 +1,35 @@
|
||||
import setuptools
|
||||
import os
|
||||
|
||||
__name = "yoshi_seals"
|
||||
|
||||
__version_sufix = os.environ.get('VERSION_SUFIX')
|
||||
if not __version_sufix:
|
||||
__version_sufix = "dev"
|
||||
|
||||
__version = f"2.0.{__version_sufix}"
|
||||
|
||||
with open("README.md", "r") as fh:
|
||||
long_description = fh.read()
|
||||
|
||||
setuptools.setup(
|
||||
name=__name,
|
||||
version=__version,
|
||||
author="Vitor Hideyoshi",
|
||||
author_email="vitor.h.n.batista@gmail.com",
|
||||
description="Numeric Calculus python module in the topic of Linear Algebra",
|
||||
long_description=long_description,
|
||||
long_description_content_type="text/markdown",
|
||||
url="https://github.com/HideyoshiNakazone/Seals-NumericCalculus.git",
|
||||
packages=setuptools.find_packages(),
|
||||
classifiers=[
|
||||
"Programming Language :: Python :: 3",
|
||||
"License :: OSI Approved :: GNU General Public License v2 (GPLv2)",
|
||||
"Operating System :: OS Independent"
|
||||
],
|
||||
python_requires='>=3.6',
|
||||
install_requires=[
|
||||
'numpy',
|
||||
'pandas',
|
||||
],
|
||||
)
|
||||
29
setup_cython.py
Normal file
29
setup_cython.py
Normal file
@@ -0,0 +1,29 @@
|
||||
import numpy
|
||||
from Cython.Build import cythonize
|
||||
from setuptools import Extension, setup
|
||||
|
||||
ext_modules = [
|
||||
Extension(
|
||||
"yoshi_seals.shared.array",
|
||||
[
|
||||
"yoshi_seals/shared/array.pyx",
|
||||
],
|
||||
extra_compile_args=["-O3", "-fopenmp"],
|
||||
extra_link_args=['-fopenmp'],
|
||||
define_macros=[("NPY_NO_DEPRECATED_API", "NPY_1_7_API_VERSION")],
|
||||
),
|
||||
Extension(
|
||||
"yoshi_seals.process.process",
|
||||
[
|
||||
"yoshi_seals/process/process.pyx",
|
||||
],
|
||||
extra_compile_args=["-O3", "-fopenmp"],
|
||||
extra_link_args=['-fopenmp'],
|
||||
define_macros=[("NPY_NO_DEPRECATED_API", "NPY_1_7_API_VERSION")]
|
||||
),
|
||||
]
|
||||
|
||||
setup(
|
||||
ext_modules=cythonize(ext_modules),
|
||||
include_dirs=[numpy.get_include()]
|
||||
)
|
||||
0
test/__init__.py
Normal file
0
test/__init__.py
Normal file
90
test/test_process.py
Normal file
90
test/test_process.py
Normal file
@@ -0,0 +1,90 @@
|
||||
from yoshi_seals import process as ps
|
||||
|
||||
import numpy as np
|
||||
|
||||
from numpy import testing as npt
|
||||
import unittest
|
||||
|
||||
|
||||
class TestProcess(unittest.TestCase):
|
||||
|
||||
def test_det(self):
|
||||
a = np.array(([1., 0.], [0., 1.]))
|
||||
|
||||
a_det = ps.det(a)
|
||||
|
||||
self.assertEqual(a_det, 1)
|
||||
|
||||
def test_inverse(self):
|
||||
a = np.array(([1., 0.], [0., 1.]))
|
||||
a_inv = ps.inverse(a)
|
||||
|
||||
npt.assert_array_equal(a, a_inv)
|
||||
|
||||
def test_hstack_returns_stacked_array(self):
|
||||
a = np.array(([1., ], [1., ]))
|
||||
b = np.array(([1., ], [1., ]))
|
||||
|
||||
stacked = ps.hstack(a, b)
|
||||
expected_stacked_array = np.array(([1., 1.], [1., 1.]))
|
||||
|
||||
npt.assert_array_equal(stacked, expected_stacked_array)
|
||||
|
||||
def test_hstack_throws_exception(self):
|
||||
a = np.array(([1., ], [1., ]))
|
||||
b = np.array(([1., ]))
|
||||
|
||||
with self.assertRaises(ValueError):
|
||||
ps.hstack(a, b)
|
||||
|
||||
def test_vstack_returns_stacked_array(self):
|
||||
a = np.array(([[1., 1.]]))
|
||||
b = np.array(([[1., 1.]]))
|
||||
|
||||
stacked = ps.vstack(a, b)
|
||||
expected_stacked_array = np.array(([1., 1.], [1., 1.]))
|
||||
|
||||
npt.assert_array_equal(stacked, expected_stacked_array)
|
||||
|
||||
def test_vstack_throws_exception(self):
|
||||
a = np.array(([[1., 1.]]))
|
||||
b = np.array(([[1.]]))
|
||||
|
||||
with self.assertRaises(ValueError):
|
||||
ps.vstack(a, b)
|
||||
|
||||
def test_gauss(self):
|
||||
a = np.array(([4., 10., 8.], [10., 26., 26], [8., 26., 61.]))
|
||||
b = np.array(([44.], [128.], [214.]))
|
||||
|
||||
matrix = ps.gauss(a, b)
|
||||
expected_matrix = np.array([[-8.], [6.], [2.]])
|
||||
|
||||
npt.assert_almost_equal(matrix, expected_matrix)
|
||||
|
||||
def test_cholesky(self):
|
||||
a = np.array(([4., 10., 8.], [10., 26., 26], [8., 26., 61.]))
|
||||
b = np.array(([44.], [128.], [214.]))
|
||||
|
||||
matrix = ps.cholesky(a, b)
|
||||
expected_matrix = np.array([[-8.], [6.], [2.]])
|
||||
|
||||
npt.assert_almost_equal(matrix, expected_matrix)
|
||||
|
||||
def test_decomposition(self):
|
||||
a = np.array(([4., 10., 8.], [10., 26., 26], [8., 26., 61.]))
|
||||
b = np.array(([44.], [128.], [214.]))
|
||||
|
||||
matrix = ps.decomposition(a, b)
|
||||
expected_matrix = np.array([[-8.], [6.], [2.]])
|
||||
|
||||
npt.assert_almost_equal(matrix, expected_matrix)
|
||||
|
||||
def test_cramer(self):
|
||||
a = np.array(([4., 10., 8.], [10., 26., 26], [8., 26., 61.]))
|
||||
b = np.array(([44.], [128.], [214.]))
|
||||
|
||||
matrix = ps.cramer(a, b)
|
||||
expected_matrix = np.array([[-8.], [6.], [2.]])
|
||||
|
||||
npt.assert_almost_equal(matrix, expected_matrix)
|
||||
@@ -1,48 +0,0 @@
|
||||
# Seals - Program made for educational intent, can be freely distributed
|
||||
# and can be used for economical intent. I will not take legal actions
|
||||
# unless my intelectual propperty, the code, is stolen or change without permission.
|
||||
|
||||
# Copyright (C) 2020 VItor Hideyoshi Nakazone Batista
|
||||
|
||||
# This program is free software; you can redistribute it and/or modify
|
||||
# it under the terms of the GNU General Public License version 2 as published by
|
||||
# the Free Software Foundation.
|
||||
|
||||
# This program is distributed in the hope that it will be useful,
|
||||
# but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
# GNU General Public License for more details.
|
||||
|
||||
# You should have received a copy of the GNU General Public License along
|
||||
# with this program; if not, write to the Free Software Foundation, Inc.,
|
||||
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
|
||||
|
||||
import numpy as np
|
||||
|
||||
def matrix(matrix: np.ndarray) -> np.ndarray:
|
||||
|
||||
i = 0
|
||||
|
||||
while (i < matrix.shape[0]):
|
||||
|
||||
j = 0
|
||||
|
||||
while (j < matrix.shape[1]):
|
||||
|
||||
matrix[i][j] = float(input('Insira o elemento {}x{}: '.format((i+1),(j+1))))
|
||||
j += 1
|
||||
|
||||
i += 1
|
||||
|
||||
return matrix
|
||||
|
||||
def vector(vector: np.ndarray) -> np.ndarray:
|
||||
|
||||
j=0
|
||||
|
||||
while (j < vector.shape[0]):
|
||||
|
||||
vector[j] = float(input('Insira o elemento b{}: '.format((j+1))))
|
||||
j += 1
|
||||
|
||||
return vector
|
||||
@@ -1,239 +0,0 @@
|
||||
# Seals - Program made for educational intent, can be freely distributed
|
||||
# and can be used for economical intent. I will not take legal actions
|
||||
# unless my intelectual propperty, the code, is stolen or change without permission.
|
||||
|
||||
# Copyright (C) 2020 VItor Hideyoshi Nakazone Batista
|
||||
|
||||
# This program is free software; you can redistribute it and/or modify
|
||||
# it under the terms of the GNU General Public License version 2 as published by
|
||||
# the Free Software Foundation.
|
||||
|
||||
# This program is distributed in the hope that it will be useful,
|
||||
# but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
# GNU General Public License for more details.
|
||||
|
||||
# You should have received a copy of the GNU General Public License along
|
||||
# with this program; if not, write to the Free Software Foundation, Inc.,
|
||||
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
|
||||
|
||||
import numpy as np
|
||||
import math
|
||||
|
||||
def identity(matrix: np.ndarray) -> np.ndarray:
|
||||
|
||||
i = 0
|
||||
|
||||
while (i < matrix.shape[0]):
|
||||
|
||||
j = 0
|
||||
|
||||
while (j < matrix.shape[0]):
|
||||
|
||||
if (i == j):
|
||||
|
||||
matrix[i][j] = 1
|
||||
|
||||
elif (i != j):
|
||||
|
||||
matrix[i][j] = 0
|
||||
|
||||
j += 1
|
||||
|
||||
i += 1
|
||||
|
||||
return matrix
|
||||
|
||||
def gauss(a: np.ndarray) -> np.ndarray:
|
||||
|
||||
for i in range(a.shape[0]):
|
||||
|
||||
k = i + 1
|
||||
|
||||
while (k<a.shape[0]):
|
||||
|
||||
l = 1
|
||||
while (a[i][i] == 0 and l < a.shape[0]):
|
||||
|
||||
temp = a[i].copy()
|
||||
a[i] = a[i+l]
|
||||
a[i+l] = temp
|
||||
|
||||
l += 1
|
||||
|
||||
if (a[k][i] == 0):
|
||||
while (k < a.shape[0]):
|
||||
k += 1
|
||||
|
||||
else:
|
||||
|
||||
a[k] = a[k] - (a[k][i]/a[i][i])*a[i]
|
||||
k += 1
|
||||
|
||||
i += 1
|
||||
|
||||
x = np.zeros(a.shape[0])
|
||||
|
||||
for j in reversed(range(x.shape[0])):
|
||||
|
||||
suma = 0
|
||||
for k in range (j,a.shape[0]):
|
||||
suma += a[j][k]*x[k]
|
||||
x[j] = (a[j][a.shape[0]] - suma)/a[j][j]
|
||||
|
||||
return x
|
||||
|
||||
def inverse(matrix: np.ndarray) -> np.ndarray:
|
||||
|
||||
matrix = np.hstack(matrix,np.identity(matrix.shape[0]))
|
||||
i = 0
|
||||
k = 0
|
||||
|
||||
while (i < matrix.shape[0]):
|
||||
|
||||
if (matrix[i][i] == 0):
|
||||
|
||||
n = i
|
||||
|
||||
while (matrix[i][i] == 0) and (n < matrix.shape[0]):
|
||||
|
||||
temp = matrix[i].copy()
|
||||
matrix[i] = matrix[n]
|
||||
matrix[n] = temp
|
||||
|
||||
n += 1
|
||||
|
||||
while (k < matrix.shape[0]):
|
||||
|
||||
if (k == i) or (matrix[k][i] == 0) or (matrix[i][i] == 0):
|
||||
|
||||
k += 1
|
||||
|
||||
else:
|
||||
|
||||
mult = matrix[k][i]/matrix[i][i]
|
||||
matrix[k] = matrix[k] - mult*matrix[i]
|
||||
k += 1
|
||||
|
||||
i += 1
|
||||
k = 0
|
||||
|
||||
i = 0
|
||||
|
||||
while ((i) < matrix.shape[0]) and (matrix[i][i] != 0):
|
||||
|
||||
matrix[i] = matrix[i]/matrix[i][i]
|
||||
i += 1
|
||||
|
||||
return matrix[:,(matrix.shape[0]):]
|
||||
|
||||
def cholesky(A: np.ndarray, b: np.ndarray) -> np.ndarray:
|
||||
|
||||
g = np.zeros((A.shape))
|
||||
|
||||
i = 0
|
||||
j = 0
|
||||
|
||||
while j < A.shape[1]:
|
||||
while i < A.shape[0]:
|
||||
|
||||
if i == 0 and j == 0:
|
||||
|
||||
g[i][j] = math.sqrt(A[0][0])
|
||||
|
||||
elif j == 0:
|
||||
|
||||
g[i][j] = A[i][0]/g[0][0]
|
||||
|
||||
elif i == j:
|
||||
|
||||
k = 0
|
||||
theta = 0
|
||||
|
||||
while k < i:
|
||||
|
||||
theta += g[i][k]**2
|
||||
k += 1
|
||||
|
||||
g[i][j] = math.sqrt(A[i][i] - theta)
|
||||
|
||||
else:
|
||||
|
||||
k = 0
|
||||
theta = 0
|
||||
|
||||
while k < j:
|
||||
|
||||
theta += g[i][k]*g[j][k]
|
||||
k += 1
|
||||
|
||||
g[i][j] = (A[i][j] - theta)/g[j][j]
|
||||
|
||||
i += 1
|
||||
|
||||
j += 1
|
||||
i = j
|
||||
|
||||
y = (inverse(g)).dot(b)
|
||||
|
||||
x = (inverse(g.T)).dot(y)
|
||||
|
||||
return x
|
||||
|
||||
def decomposition(U: np.ndarray, b: np.ndarray) -> np.ndarray:
|
||||
|
||||
L = identity(np.zeros(U.shape))
|
||||
|
||||
i = 0
|
||||
k = 0
|
||||
|
||||
while (i < U.shape[0]):
|
||||
|
||||
k = 0
|
||||
|
||||
if (U[i][i] == 0):
|
||||
|
||||
n = i
|
||||
|
||||
while (U[i][i] == 0) and (n < U.shape[0]):
|
||||
|
||||
temp = U[i].copy()
|
||||
U[i] = U[n]
|
||||
U[n] = temp
|
||||
|
||||
n += 1
|
||||
|
||||
while (k < U.shape[0]):
|
||||
|
||||
if (k <= i) or (U[i][i] == 0):
|
||||
|
||||
k += 1
|
||||
|
||||
else:
|
||||
L[k][i] = U[k][i]/U[i][i]
|
||||
U[k] = U[k] - L[k][i]*U[i]
|
||||
k += 1
|
||||
|
||||
i += 1
|
||||
|
||||
y = (inverse(L)).dot(b)
|
||||
|
||||
x = (inverse(U)).dot(y)
|
||||
|
||||
return x
|
||||
|
||||
def cramer(A: np.ndarray, b: np.ndarray) -> np.ndarray:
|
||||
|
||||
x = np.vstack(np.zeros(b.shape))
|
||||
k = 0
|
||||
|
||||
while (k < A.shape[0]):
|
||||
|
||||
temp = A.copy()
|
||||
temp[:,k] = b
|
||||
|
||||
x[k] = np.linalg.det(temp)/np.linalg.det(A)
|
||||
|
||||
k += 1
|
||||
|
||||
return x
|
||||
0
yoshi_seals/__init__.pxd
Normal file
0
yoshi_seals/__init__.pxd
Normal file
@@ -19,23 +19,21 @@
|
||||
|
||||
import numpy as np
|
||||
|
||||
def eigen(a: np.ndarray) -> np.ndarray:
|
||||
|
||||
k = 0
|
||||
def eigen(a: np.ndarray) -> np.ndarray:
|
||||
l = np.ones((a.shape[0]))
|
||||
|
||||
at = a # variavel temporaria para A
|
||||
b = np.random.rand(a.shape[0], a.shape[1])
|
||||
|
||||
while (k < at.shape[0]):
|
||||
for k in range(at.shape[0]):
|
||||
|
||||
u = np.random.rand(at.shape[0], 1)
|
||||
u = u / max(u.min(), u.max(), key=abs)
|
||||
|
||||
ctrl = 0
|
||||
|
||||
while (ctrl != l[k]):
|
||||
|
||||
while ctrl != l[k]:
|
||||
ctrl = l[k]
|
||||
u = at.dot(u)
|
||||
l[k] = max(u.min(), u.max(), key=abs)
|
||||
@@ -55,10 +53,9 @@ def eigen(a: np.ndarray) -> np.ndarray:
|
||||
|
||||
i = 0
|
||||
|
||||
while (u[i] == 0):
|
||||
while u[i] == 0:
|
||||
i += 1
|
||||
|
||||
at = at - (1 / u[i]) * u * at[i]
|
||||
k += 1
|
||||
|
||||
return l, b
|
||||
@@ -17,9 +17,21 @@
|
||||
# with this program; if not, write to the Free Software Foundation, Inc.,
|
||||
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
|
||||
|
||||
from .process import identity
|
||||
from .process import gauss
|
||||
from .process import inverse
|
||||
from .process import cholesky
|
||||
from .process import decomposition
|
||||
from .process import cramer
|
||||
import numpy as np
|
||||
|
||||
|
||||
def matrix(a: np.ndarray) -> np.ndarray:
|
||||
|
||||
for i in range(a.shape[0]):
|
||||
for j in range(a.shape[1]):
|
||||
a[i][j] = float(input(f"Insert the element a{i+1}x{j+1}: "))
|
||||
|
||||
return a
|
||||
|
||||
|
||||
def vector(v: np.ndarray) -> np.ndarray:
|
||||
|
||||
for j in range(v.shape[0]):
|
||||
v[j] = float(input(f"Insert the element b{j+1}: "))
|
||||
|
||||
return v
|
||||
35
yoshi_seals/process/__init__.py
Normal file
35
yoshi_seals/process/__init__.py
Normal file
@@ -0,0 +1,35 @@
|
||||
import yoshi_seals.process.process as ps
|
||||
|
||||
import numpy as np
|
||||
|
||||
|
||||
def det(a: np.ndarray) -> float:
|
||||
return ps.det(a)
|
||||
|
||||
|
||||
def inverse(a: np.ndarray) -> np.ndarray:
|
||||
return ps.inverse(a)
|
||||
|
||||
|
||||
def hstack(a: np.ndarray, b: np.ndarray) -> np.ndarray:
|
||||
return ps.hstack(a, b)
|
||||
|
||||
|
||||
def vstack(a: np.ndarray, b: np.ndarray) -> np.ndarray:
|
||||
return ps.vstack(a, b)
|
||||
|
||||
|
||||
def gauss(a: np.ndarray, b: np.ndarray) -> np.ndarray:
|
||||
return ps.gauss(a, b)
|
||||
|
||||
|
||||
def cholesky(a: np.ndarray, b: np.ndarray) -> np.ndarray:
|
||||
return ps.cholesky(a, b)
|
||||
|
||||
|
||||
def decomposition(a: np.ndarray, b: np.ndarray) -> np.ndarray:
|
||||
return ps.decomposition(a, b)
|
||||
|
||||
|
||||
def cramer(a: np.ndarray, b: np.ndarray) -> np.ndarray:
|
||||
return ps.cramer(a, b)
|
||||
193
yoshi_seals/process/process.pyx
Normal file
193
yoshi_seals/process/process.pyx
Normal file
@@ -0,0 +1,193 @@
|
||||
# Seals - Program made for educational intent, can be freely distributed
|
||||
# and can be used for economical intent. I will not take legal actions
|
||||
# unless my intelectual propperty, the code, is stolen or change without permission.
|
||||
|
||||
# Copyright (C) 2020 VItor Hideyoshi Nakazone Batista
|
||||
|
||||
# This program is free software; you can redistribute it and/or modify
|
||||
# it under the terms of the GNU General Public License version 2 as published by
|
||||
# the Free Software Foundation.
|
||||
|
||||
# This program is distributed in the hope that it will be useful,
|
||||
# but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
# GNU General Public License for more details.
|
||||
|
||||
# You should have received a copy of the GNU General Public License along
|
||||
# with this program; if not, write to the Free Software Foundation, Inc.,
|
||||
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
|
||||
|
||||
from yoshi_seals.shared cimport array
|
||||
|
||||
from libc.stdlib cimport malloc
|
||||
from libc cimport math
|
||||
|
||||
cimport numpy as np
|
||||
import numpy as np
|
||||
|
||||
cpdef double det(double[::,::] a):
|
||||
return array.det(a)
|
||||
|
||||
cpdef np.ndarray[np.float64_t, ndim=2] inverse(double[::,::] matrix):
|
||||
return np.asarray(array.inverse(matrix))
|
||||
|
||||
cpdef np.ndarray[np.float64_t, ndim=2] hstack(double[::,::] a, double[::,::] b):
|
||||
return np.asarray(array.hstack(a, b))
|
||||
|
||||
cpdef np.ndarray[np.float64_t, ndim=2] vstack(double[::,::] a, double[::,::] b):
|
||||
return np.asarray(array.vstack(a, b))
|
||||
|
||||
cpdef np.ndarray[np.float64_t, ndim=2] gauss(double[::,::] A, double[::,::] b):
|
||||
|
||||
cdef:
|
||||
int i = 0, j = 0, k = 0, l = 0, reversed_index = 0
|
||||
|
||||
double[:] tmp
|
||||
double sum_var
|
||||
|
||||
double[::,::] a = array.hstack(A,b)
|
||||
|
||||
double *c_pointer = <double *> malloc(A.shape[1]*sizeof(double))
|
||||
double[:] x = <double[:A.shape[1]]>c_pointer
|
||||
|
||||
if not c_pointer:
|
||||
raise MemoryError()
|
||||
|
||||
for i in range(A.shape[0]):
|
||||
|
||||
l = 1
|
||||
while i < A.shape[1] and a[i][i] == 0 and (l + i) < A.shape[0]:
|
||||
|
||||
tmp = a[i]
|
||||
a[i] = a[i+l]
|
||||
a[i+l] = tmp
|
||||
|
||||
l += 1
|
||||
|
||||
for k in range(i + 1, A.shape[1]):
|
||||
|
||||
if a[k][i] != 0:
|
||||
a[k] = array.subtract(a[k],array.mult(a[i], (a[k][i]/a[i][i])))
|
||||
|
||||
for j in range(A.shape[1]):
|
||||
|
||||
sum_var = 0
|
||||
reversed_index = (A.shape[1] - 1) - j
|
||||
|
||||
for k in range(reversed_index,A.shape[1]):
|
||||
sum_var += a[reversed_index][k]*x[k]
|
||||
x[reversed_index] = (a[reversed_index][A.shape[1]] - sum_var)/a[reversed_index][reversed_index]
|
||||
|
||||
return np.asarray(x).reshape(b.shape[0],b.shape[1])
|
||||
|
||||
cpdef np.ndarray[np.float64_t, ndim=2] cholesky(double[:,:] A, double[:,:] b):
|
||||
|
||||
cdef:
|
||||
int i = 0, j = 0, size_x = A.shape[0], size_y = A.shape[1]
|
||||
|
||||
double *c_pointer = <double *> malloc(size_x*size_y*sizeof(double))
|
||||
double[::,::] g = <double[:size_x,:size_y]>c_pointer, y, x
|
||||
|
||||
while j < size_y:
|
||||
while i < size_x:
|
||||
if i == 0 and j == 0:
|
||||
g[i][j] = math.sqrt(A[0][0])
|
||||
|
||||
elif j == 0:
|
||||
g[i][j] = A[i][0] / g[0][0]
|
||||
|
||||
elif i == j:
|
||||
k = 0
|
||||
theta = 0
|
||||
|
||||
while k < i:
|
||||
theta += g[i][k] ** 2
|
||||
k += 1
|
||||
|
||||
g[i][j] = math.sqrt(A[i][i] - theta)
|
||||
|
||||
else:
|
||||
k = 0
|
||||
theta = 0
|
||||
|
||||
while k < j:
|
||||
theta += g[i][k] * g[j][k]
|
||||
k += 1
|
||||
|
||||
g[i][j] = (A[i][j] - theta) / g[j][j]
|
||||
|
||||
i += 1
|
||||
|
||||
j += 1
|
||||
i = j
|
||||
|
||||
y = array.dot(array.inverse(g), b)
|
||||
|
||||
x = array.dot(array.inverse(array.transpose(g)), y)
|
||||
|
||||
return np.asarray(x)
|
||||
|
||||
|
||||
cpdef np.ndarray[np.float64_t, ndim=2] decomposition(double[::,::] U, double[::,::] b):
|
||||
|
||||
cdef:
|
||||
int i = 0, k = 0
|
||||
|
||||
double[::,::] L = array.identity(U.shape[0]), y, x
|
||||
|
||||
for i in range(U.shape[0]):
|
||||
|
||||
if U[i][i] == 0:
|
||||
|
||||
n = i
|
||||
|
||||
while (U[i][i] == 0) and (n < U.shape[0]):
|
||||
temp = U[i].copy()
|
||||
U[i] = U[n]
|
||||
U[n] = temp
|
||||
|
||||
n += 1
|
||||
|
||||
for k in range(U.shape[0]):
|
||||
|
||||
if (k > i) and (U[i][i] != 0):
|
||||
|
||||
L[k][i] = U[k][i] / U[i][i]
|
||||
U[k] = array.subtract(U[k], array.mult(U[i], L[k][i]))
|
||||
|
||||
y = array.dot(array.inverse(L), b)
|
||||
|
||||
x = array.dot(array.inverse(U), y)
|
||||
|
||||
return np.asarray(x)
|
||||
|
||||
cpdef np.ndarray[np.float64_t, ndim=2] cramer(double[:,:] A, double[:,:] b):
|
||||
|
||||
cdef:
|
||||
int size_a_y = A.shape[0], size_a_x = A.shape[1]
|
||||
int size_b_y = b.shape[0], size_b_x = b.shape[1]
|
||||
|
||||
int k = 0
|
||||
|
||||
double *c_pointer_tmp = <double *> malloc(size_a_x*size_a_y*sizeof(double))
|
||||
double[::,::] tmp = <double[:size_a_y,:size_a_x]>c_pointer_tmp
|
||||
|
||||
double *c_pointer_x = <double *> malloc(size_b_x*size_b_y*sizeof(double))
|
||||
double[::,::] x = <double[:size_b_y,:size_b_x]>c_pointer_x
|
||||
|
||||
if size_a_y != size_b_y:
|
||||
raise ValueError("The matrices must have the same height.")
|
||||
if size_b_x != 1:
|
||||
raise ValueError("The b matrix must be a column matrix.")
|
||||
|
||||
for k in range(size_a_x):
|
||||
tmp = A.copy()
|
||||
|
||||
for i in range(size_a_y):
|
||||
tmp[i, k] = b[i,0]
|
||||
|
||||
x[k,0] = np.linalg.det(tmp) / np.linalg.det(A)
|
||||
|
||||
k += 1
|
||||
|
||||
return np.asarray(x)
|
||||
@@ -20,26 +20,13 @@
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
def numpy(path:str, sep: str = None, decimal: str = None) -> np.ndarray:
|
||||
|
||||
if sep is None:
|
||||
sep = ","
|
||||
|
||||
if decimal is None:
|
||||
decimal = "."
|
||||
|
||||
def numpy(path: str, sep: str = ",", decimal: str = ".") -> np.ndarray:
|
||||
df = pd.read_csv(path, sep=sep, decimal=decimal, header=None)
|
||||
array = df.to_numpy()
|
||||
|
||||
return array
|
||||
|
||||
def pandas(path: str, sep: str = None, decimal: str = None) -> pd.DataFrame:
|
||||
|
||||
if sep is None:
|
||||
sep = ","
|
||||
|
||||
if decimal is None:
|
||||
decimal = "."
|
||||
|
||||
def pandas(path: str, sep: str = ",", decimal: str = ".") -> pd.DataFrame:
|
||||
return pd.read_csv(path, sep=sep, decimal=decimal)
|
||||
|
||||
0
yoshi_seals/shared/__init__.pxd
Normal file
0
yoshi_seals/shared/__init__.pxd
Normal file
0
yoshi_seals/shared/__init__.py
Normal file
0
yoshi_seals/shared/__init__.py
Normal file
25
yoshi_seals/shared/array.pxd
Normal file
25
yoshi_seals/shared/array.pxd
Normal file
@@ -0,0 +1,25 @@
|
||||
cdef double[::] mult(double[::] array, double value)
|
||||
|
||||
cdef double[::] div(double[::] array, double value)
|
||||
|
||||
cdef double[::] addition(double[::] a, double[::] b)
|
||||
|
||||
cdef double[::] subtract(double[::] a, double[::] b)
|
||||
|
||||
cdef double[::,:] hstack(double[:,:] a, double[:,:] b)
|
||||
|
||||
cdef double[::,::] vstack(double[:,:] a, double[:,:] b)
|
||||
|
||||
cdef double[::,::] identity(int size)
|
||||
|
||||
cdef double[:,:] zeros((int, int) sizes)
|
||||
|
||||
cdef double[:,:] ones((int, int) sizes)
|
||||
|
||||
cdef double[:,:] inverse(double[:,:] a)
|
||||
|
||||
cdef double[:,:] transpose(double[:,:] a)
|
||||
|
||||
cdef double[:,:] dot(double[:,:] a, double[:,:] b)
|
||||
|
||||
cdef double det(double[::,::] a)
|
||||
251
yoshi_seals/shared/array.pyx
Normal file
251
yoshi_seals/shared/array.pyx
Normal file
@@ -0,0 +1,251 @@
|
||||
from libc.stdlib cimport malloc
|
||||
|
||||
cimport numpy as np
|
||||
import numpy as np
|
||||
|
||||
|
||||
cdef double[::] mult(double[::] array, double value):
|
||||
|
||||
cdef int size = array.shape[0], i = 0
|
||||
|
||||
cdef:
|
||||
double *c_pointer = <double *> malloc(size*sizeof(double))
|
||||
double[::] mult_array = <double[:size]>c_pointer
|
||||
|
||||
for i in range(size):
|
||||
|
||||
mult_array[i] = array[i]*value
|
||||
|
||||
return mult_array
|
||||
|
||||
cdef double[::] div(double[::] array, double value):
|
||||
|
||||
cdef int size = array.shape[0], i = 0
|
||||
|
||||
cdef:
|
||||
double *c_pointer = <double *> malloc(size*sizeof(double))
|
||||
double[::] mult_array = <double[:size]>c_pointer
|
||||
|
||||
for i in range(size):
|
||||
|
||||
mult_array[i] = array[i]/value
|
||||
|
||||
return mult_array
|
||||
|
||||
cdef double[::] addition(double[::] a, double[::] b):
|
||||
|
||||
cdef int size_a = a.shape[0], size_b = b.shape[0], i = 0
|
||||
cdef double *c_pointer = <double *> malloc(size_a*sizeof(double))
|
||||
cdef double[::] mult_array = <double[:size_a]>c_pointer
|
||||
|
||||
for i in range(size_a):
|
||||
|
||||
mult_array[i] = a[i] + b[i]
|
||||
|
||||
return mult_array
|
||||
|
||||
cdef double[::] subtract(double[::] a, double[::] b):
|
||||
|
||||
cdef int size_a = a.shape[0], size_b = b.shape[0], i = 0
|
||||
cdef double *c_pointer = <double *> malloc(size_a*sizeof(double))
|
||||
cdef double[::] mult_array = <double[:size_a]>c_pointer
|
||||
|
||||
for i in range(size_a):
|
||||
|
||||
mult_array[i] = a[i] - b[i]
|
||||
|
||||
return mult_array
|
||||
|
||||
cdef double[::,::] identity(int size):
|
||||
|
||||
cdef int i = 0
|
||||
|
||||
cdef double *c_pointer = <double *> malloc(size*size*sizeof(double))
|
||||
cdef double[::,:] matrix = <double[:size,:size]>c_pointer
|
||||
|
||||
for i in range(size):
|
||||
for j in range(size):
|
||||
if i == j:
|
||||
matrix[i][j] = 1
|
||||
|
||||
elif i != j:
|
||||
matrix[i][j] = 0
|
||||
|
||||
return matrix
|
||||
|
||||
cdef double[:,:] zeros((int, int) size):
|
||||
|
||||
cdef int i = 0, j = 0
|
||||
|
||||
cdef:
|
||||
|
||||
double *c_pointer = <double *> malloc(size[0]*size[1]*sizeof(double))
|
||||
double[:,:] id_array = <double[:size[0],:size[1]]>c_pointer
|
||||
|
||||
if not c_pointer:
|
||||
raise MemoryError()
|
||||
|
||||
for i in range(size[0]):
|
||||
for j in range(size[1]):
|
||||
id_array[i,j] = 0.0
|
||||
|
||||
return id_array
|
||||
|
||||
cdef double[:,:] ones((int, int) size):
|
||||
|
||||
cdef int i = 0, j = 0
|
||||
|
||||
cdef:
|
||||
|
||||
double *c_pointer = <double *> malloc(size[0]*size[1]*sizeof(double))
|
||||
double[:,:] id_array = <double[:size[0],:size[1]]>c_pointer
|
||||
|
||||
for i in range(size[0]):
|
||||
for j in range(size[1]):
|
||||
id_array[i,j] = 1.0
|
||||
|
||||
return id_array
|
||||
|
||||
cdef double[:,:] hstack(double[:,:] a, double[:,:] b):
|
||||
|
||||
cdef:
|
||||
|
||||
int i, j
|
||||
int a_x = a.shape[0], a_y = a.shape[1]
|
||||
int b_x = b.shape[0], b_y = b.shape[1]
|
||||
int size_x = a_x, size_y = a_y + b_y
|
||||
|
||||
double *c_pointer = <double *> malloc(size_x*size_y*sizeof(double))
|
||||
double[::,::] matrix = <double[:size_x,:size_y]>c_pointer
|
||||
|
||||
if a_x != b_x:
|
||||
raise ValueError("Cannot hstack matrices")
|
||||
|
||||
for i in range(size_x):
|
||||
for j in range(size_y):
|
||||
if j < a_y:
|
||||
matrix[i,j] = a[i,j]
|
||||
else:
|
||||
matrix[i,j] = b[i,j-a_y]
|
||||
|
||||
return matrix
|
||||
|
||||
cdef double[:,:] vstack(double[:,:] a, double[:,:] b):
|
||||
|
||||
cdef:
|
||||
|
||||
int i, j
|
||||
int a_x = a.shape[0], b_x = b.shape[0]
|
||||
int a_y = a.shape[1], b_y = b.shape[1]
|
||||
int size_x = a_x + b_x, size_y = a_y
|
||||
|
||||
double *c_pointer = <double *> malloc(size_x*size_y*sizeof(double))
|
||||
double[:,:] matrix = <double[:size_x,:size_y]>c_pointer
|
||||
|
||||
if a_y != b_y:
|
||||
raise ValueError("Cannot vstack matrices")
|
||||
|
||||
for i in range(size_x):
|
||||
for j in range(size_y,):
|
||||
if i < a_x:
|
||||
matrix[i,j] = a[i,j]
|
||||
else:
|
||||
matrix[i,j] = b[i-a_x, j]
|
||||
|
||||
return matrix
|
||||
|
||||
cdef double[:,:] inverse(double[:,:] a):
|
||||
|
||||
cdef:
|
||||
|
||||
int i = 0, k = 0, n, size = a.shape[0]
|
||||
|
||||
double[:,:] matrix = hstack(a,identity(size))
|
||||
double mult_const
|
||||
double[:] tmp
|
||||
|
||||
if a.shape[0] != a.shape[1]:
|
||||
raise ValueError("Non Quadratic Matrix doesn't have an Inverse Matrix")
|
||||
|
||||
for i in range(size):
|
||||
|
||||
if matrix[i][i] == 0:
|
||||
|
||||
n = i
|
||||
|
||||
while (matrix[i][i] == 0) and (n < size):
|
||||
|
||||
tmp = matrix[i]
|
||||
matrix[i] = matrix[n].copy()
|
||||
matrix[n] = tmp
|
||||
|
||||
n += 1
|
||||
|
||||
for k in range(size):
|
||||
|
||||
if (k != i) and (matrix[i][i] != 0):
|
||||
|
||||
mult_const = matrix[k][i]/matrix[i][i]
|
||||
matrix[k] = subtract(matrix[k], mult(matrix[i], mult_const))
|
||||
|
||||
for k in range(size):
|
||||
if matrix[k][k] != 0:
|
||||
matrix[k] = div(matrix[k], matrix[k][k])
|
||||
|
||||
return matrix[:,size:]
|
||||
|
||||
cdef double[:,:] transpose(double[:,:] a):
|
||||
|
||||
cdef:
|
||||
|
||||
int size_x = a.shape[0], size_y = a.shape[1]
|
||||
|
||||
double *c_pointer = <double *> malloc(size_y*size_x*sizeof(double))
|
||||
double[:,:] tmp = <double[:size_y,:size_x]>c_pointer
|
||||
|
||||
for i in range(size_x):
|
||||
for j in range(size_y):
|
||||
tmp[j,i] = a[i,j]
|
||||
|
||||
return tmp
|
||||
|
||||
cdef double[:,:] dot(double[:,:] a, double[:,:] b):
|
||||
c = np.zeros((a.shape[0], b.shape[1]))
|
||||
|
||||
for i in range(a.shape[0]):
|
||||
for j in range(b.shape[1]):
|
||||
for k in range(a.shape[0]):
|
||||
c[i][j] += a[i][k] * b[k][j]
|
||||
|
||||
return c
|
||||
|
||||
cdef double det(double[::,::] a):
|
||||
|
||||
cdef:
|
||||
|
||||
double[:,:] tmp
|
||||
double total = 0, sub_det
|
||||
|
||||
int size_x = a.shape[0], size_y = a.shape[1], i
|
||||
|
||||
if size_x != size_y:
|
||||
raise ValueError("Determinant Operation is only valid for Quadratic Matrices.")
|
||||
|
||||
if size_x == 2 and size_y == 2:
|
||||
total = a[0][0] * a[1][1] - a[1][0] * a[0][1]
|
||||
return total
|
||||
|
||||
for i in range(size_x):
|
||||
|
||||
tmp = a.copy()
|
||||
tmp = tmp[1:]
|
||||
|
||||
for i in range(size_y):
|
||||
tmp[i] = addition(tmp[i][0:i], tmp[i][i + 1:])
|
||||
|
||||
sign = (-1) ** (i % 2)
|
||||
sub_det = det(tmp)
|
||||
|
||||
total += sign * a[0][i] * sub_det
|
||||
|
||||
return total
|
||||
@@ -21,15 +21,14 @@ import pandas as pd
|
||||
import numpy as np
|
||||
import csv
|
||||
|
||||
|
||||
def numpy(array: np.ndarray, path: str) -> np.ndarray:
|
||||
|
||||
with open(path, mode='w') as sistema_linear:
|
||||
|
||||
solution_writer = csv.writer(sistema_linear, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
|
||||
with open(path, mode='w') as linear_system:
|
||||
solution_writer = csv.writer(linear_system, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
|
||||
solution_writer.writerows(array)
|
||||
|
||||
return array
|
||||
|
||||
def pandas(df: pd.DataFrame, path:str) -> None:
|
||||
|
||||
def pandas(df: pd.DataFrame, path: str) -> None:
|
||||
df.to_csv(path)
|
||||
Reference in New Issue
Block a user