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a/pyproject.toml +++ b/pyproject.toml @@ -12,9 +12,11 @@ readme = "README.md" python = ">=3.8,<3.9" pre-commit = "^3.4.0" pyinstaller = "^6.0.0" -pandas = "^2.0" pyside2 = "^5.15.2.1" -pylint = "^2" +pandas = { version = "^2", markers = "python_version < '3.9'" } +pylint = { version = "^2", markers = "python_version < '3.9'" } +scipy = { version = "^1", markers = "python_version < '3.9'" } +openpyxl = "^3.1.2" [build-system] diff --git a/statapp/__main__.py b/statapp/__main__.py index b106134..4372a59 100644 --- a/statapp/__main__.py +++ b/statapp/__main__.py @@ -19,9 +19,12 @@ # import sys +import numpy as np +import pandas as pd from PySide2 import QtCore from PySide2.QtWidgets import QApplication +from statapp.calculations import linearPolynom from statapp.main_window import MainWindow @@ -38,6 +41,51 @@ def main(): window.show() return app.exec_() +data = [ + [6014.700000, 2995.067340, 2982.474660], + [6031.200000, 2971.594560, 2993.546160], + [6145.800000, 3017.328000, 2970.497280], + [6133.200000, 2977.476480, 2970.796320], + [6063.600000, 3006.012960, 2982.839040], + [5934.900000, 2979.831420, 2992.026240], + [6016.800000, 2997.121680, 2984.065920], + [6070.200000, 2996.335920, 2975.729640], + [5935.800000, 2982.114960, 2976.811560], + [6108.300000, 2987.822700, 2974.378500] +] + +data = pd.DataFrame(data).to_numpy() if __name__ == "__main__": - sys.exit(main()) + n = len(data) + + # y = generateYValues(100, 5, n) + # x1 = generateXValues(20, 2, 0, y) + # x2 = generateXValues(10, 1, 0, y) + # x3 = generateXValues(5, 0, 0, y) + # y = np.array([18, 19, 22, 20, 24, 23]) + # x1 = np.array([13, 14, 15, 17, 16, 15]) + # x2 = np.array([18, 19, 22, 20, 24, 23]) + + # data = np.concatenate([y, x1, x2, x3], axis=1) + + res = linearPolynom(data) + + solution = res[0] + residues = res[1] + rank = res[2] + + SSE = np.sum(residues) + + print(SSE) + + df = n - rank + + MSE = SSE / df + + # MSE = np.mean(residues) + SSE = np.sum(residues) + + print("Остаточная дисперсия (MSE = S^2):", MSE) + + # sys.exit(main()) diff --git a/statapp/calculations.py b/statapp/calculations.py index 871fafb..621c9b4 100644 --- a/statapp/calculations.py +++ b/statapp/calculations.py @@ -24,6 +24,10 @@ DIRECT_LINK = 0 INDIRECT_LINK = 1 +def generateYValues(mean, std, count): + return np.random.normal(mean, std, size=(count, 1)) + + def generateXValues(mean, std, typeConnection, yColumn): yMean = np.mean(yColumn) values = [] @@ -38,7 +42,9 @@ def generateXValues(mean, std, typeConnection, yColumn): else: x = mean values.append(x) - return np.array(values) + + res = np.array(values) + return res.reshape(len(res), 1) def varianceAnalysis(data): @@ -49,3 +55,15 @@ def varianceAnalysis(data): def correlationAnalysis(data): return pd.DataFrame(data).corr().to_numpy() + + +def linearPolynom(data): + x = data[:, 1:] + y = data[:, [0]] + + df = pd.DataFrame(x) + df.insert(0, 'const', 1) + + x = df.to_numpy() + + return np.linalg.lstsq(x, y, rcond=None) diff --git a/statapp/main_window.py b/statapp/main_window.py index eb6172d..3c0129c 100644 --- a/statapp/main_window.py +++ b/statapp/main_window.py @@ -21,7 +21,7 @@ import numpy as np from PySide2.QtCore import Slot from PySide2.QtWidgets import QMainWindow, QMessageBox -from statapp.calculations import generateXValues +from statapp.calculations import generateXValues, generateYValues from statapp.generate_factor_window import GenerateFactorWindow from statapp.models.input_values_model import InputValuesModel from statapp.generate_window import GenerateWindow @@ -112,7 +112,7 @@ class MainWindow(QMainWindow): gw = GenerateWindow() if gw.exec(): - y = np.random.normal(gw.mat, gw.deviation, size=(gw.count, 1)) + y = generateYValues(gw.mat, gw.deviation, gw.count) self.model.updateAllData(y.round(2)) self.isDataChanged = True self.ui.generateXaction.setEnabled(True) @@ -125,8 +125,7 @@ class MainWindow(QMainWindow): data = self.model.getData() y = self.model.getY() xValues = generateXValues(gfw.mat, gfw.deviation, gfw.typeConnection, y) - xValues = xValues.reshape(len(xValues), 1).round(2) - data = np.concatenate((data, xValues), axis=1) + data = np.concatenate((data, xValues.round(2)), axis=1) self.model.updateAllData(data) self.ui.varianceAnalysisAction.setEnabled(True) self.ui.correlationAnalisisAction.setEnabled(True)