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95 lines
2.5 KiB
Python
95 lines
2.5 KiB
Python
#
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# Copyright (c) 2023 Maxim Slipenko, Eugene Lazurenko.
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#
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# This file is part of Statapp
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# (see https://github.com/shizand/statapp).
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#
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU General Public License for more details.
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#
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# You should have received a copy of the GNU General Public License
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# along with this program. If not, see <http://www.gnu.org/licenses/>.
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#
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from dataclasses import dataclass
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import numpy as np
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import pandas as pd
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DIRECT_LINK = 0
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INDIRECT_LINK = 1
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def generateYValues(mean, std, count):
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return np.random.normal(mean, std, size=(count, 1))
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def generateXValues(mean, std, typeConnection, yColumn):
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yMean = np.mean(yColumn)
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values = []
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for y in yColumn:
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raz = np.abs(mean - np.random.normal(mean, std))
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if typeConnection == INDIRECT_LINK:
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raz *= -1
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if y > yMean:
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x = mean + raz
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elif y < yMean:
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x = mean - raz
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else:
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x = mean
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values.append(x)
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res = np.array(values)
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return res.reshape(len(res), 1)
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def varianceAnalysis(data):
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return np.array([
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[np.mean(col), np.std(col), np.min(col), np.max(col)] for col in data.T
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])
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def correlationAnalysis(data):
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return pd.DataFrame(data).corr().to_numpy()
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@dataclass()
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class LinearPolynomResult:
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paramsAndImportance: np.ndarray
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residualVariance: np.float64
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def linearPolynom(inputData) -> LinearPolynomResult:
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x = inputData[:, 1:]
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y = inputData[:, 0]
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data = pd.DataFrame(x)
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data.insert(0, 'const', 1)
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# ---
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result = np.linalg.lstsq(data, y, rcond=None)
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# Коэффициенты регрессии
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params = result[0]
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# Остатки
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residues = result[1]
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# Степень свободы
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dof = len(data) - len(params)
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mse = residues / dof
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cov = mse * np.diagonal(np.linalg.inv(data.T @ data))
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se = np.sqrt(cov)
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tStatistics = params / se
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# возможно стоит сделать через np.reshape + np.concatenate
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out = pd.DataFrame()
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out[0] = params
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out[1] = tStatistics
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return LinearPolynomResult(
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out.to_numpy(),
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np.float64(mse[0])
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)
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