diff --git a/statapp/__main__.py b/statapp/__main__.py index 2abf198..5e760de 100644 --- a/statapp/__main__.py +++ b/statapp/__main__.py @@ -19,12 +19,9 @@ # import sys -import numpy as np from PySide2 import QtCore from PySide2.QtWidgets import QApplication -from statapp import calculations -from statapp.calculations import generateXValues, generateYValues from statapp.main_window import MainWindow @@ -43,18 +40,4 @@ def main(): return app.exec_() if __name__ == "__main__": - # Для быстрой отладки - N = 10 - y = generateYValues(100, 5, N) - x1 = generateXValues(20, 2, 0, y) - x2 = generateXValues(10, 1, 0, y) - - data = np.concatenate([y, x1, x2], axis=1) - - out = calculations.squaredPolynom(data) - - coef = [] - - print() - sys.exit(main()) diff --git a/statapp/_vendor/multipolyfit.py b/statapp/_vendor/multipolyfit.py index a91ed92..c96b804 100644 --- a/statapp/_vendor/multipolyfit.py +++ b/statapp/_vendor/multipolyfit.py @@ -1,6 +1,5 @@ # Copyright (c) 2023 Matthew Rocklin # All rights reserved. -import numpy as np # This source code is distributed under the terms of the BSD license, # which allows you to use, modify, and distribute it # as long as you comply with the license terms. @@ -9,11 +8,10 @@ import numpy as np # is now also licensed under the GPL-3.0. # See the GPL-3.0 license for details. -# TODO: remove -# pylint: skip-file - -from numpy import linalg, zeros, ones, hstack, asarray, diagonal import itertools +import numpy as np +from numpy import linalg, zeros, ones, hstack, asarray, diagonal +from sympy import symbols, Mul, Add, S def basisVector(n, i): @@ -29,11 +27,14 @@ def basisVector(n, i): x[i] = 1 return x + def asTall(x): """ Turns a row vector into a column vector """ return x.reshape(x.shape + (1,)) -def multipolyfit(xs, y, deg, full=False, model_out=False, powers_out=False): + +def multipolyfit(xs, y, deg, full=False, modelOut=False, powersOut=False): + # pylint: disable-msg=too-many-locals """ Least squares multivariate polynomial fit @@ -49,10 +50,10 @@ def multipolyfit(xs, y, deg, full=False, model_out=False, powers_out=False): y-coordinates of the sample points. deg : int Degree o fthe fitting polynomial - model_out : bool (defaults to True) + modelOut : bool (defaults to True) If True return a callable function If False return an array of coefficients - powers_out : bool (defaults to False) + powersOut : bool (defaults to False) Returns the meaning of each of the coefficients in the form of an iterator that gives the powers over the inputs and 1 For example if xs corresponds to the covariates a,b,c then the array @@ -63,8 +64,9 @@ def multipolyfit(xs, y, deg, full=False, model_out=False, powers_out=False): numpy.polyfit """ + # pylin y = asarray(y).squeeze() - rows = y.shape[0] + # rows = y.shape[0] xs = asarray(xs) numCovariates = xs.shape[1] xs = hstack((ones((xs.shape[0], 1), dtype=xs.dtype) , xs)) @@ -81,10 +83,10 @@ def multipolyfit(xs, y, deg, full=False, model_out=False, powers_out=False): result = linalg.lstsq(a, y, rcond=None) beta = result[0] - if model_out: - return mk_model(beta, powers) + if modelOut: + return mkModel(beta, powers) - if powers_out: + if powersOut: return beta, powers if full: @@ -100,7 +102,8 @@ def multipolyfit(xs, y, deg, full=False, model_out=False, powers_out=False): return beta -def mk_model(beta, powers): + +def mkModel(beta, powers): """ Create a callable python function out of beta/powers from multipolyfit This function is callable from within multipolyfit using the model_out flag @@ -108,23 +111,23 @@ def mk_model(beta, powers): # Create a function that takes in many x values # and returns an approximate y value def model(*args): - num_covariates = len(powers[0]) - 1 - if len(args)!=(num_covariates): - raise ValueError("Expected %d inputs"%num_covariates) + numCovariates = len(powers[0]) - 1 + if len(args) != numCovariates: + raise ValueError(f"Expected {numCovariates} inputs") xs = asarray((1,) + args) - return sum([coeff * (xs**p).prod() - for p, coeff in zip(powers, beta)]) + return sum(coeff * (xs**p).prod() + for p, coeff in zip(powers, beta)) return model -def mk_sympy_function(beta, powers): - from sympy import symbols, Add, Mul, S - terms = get_terms(powers) + +def mkSympyFunction(beta, powers): + terms = getTerms(powers) return Add(*[coeff * term for term, coeff in zip(terms, beta)]) -def get_terms(powers): - from sympy import symbols, Add, Mul, S - num_covariates = len(powers[0]) - xs = (S.One,) + symbols('x1:%d' % num_covariates) + +def getTerms(powers): + numCovariates = len(powers[0]) + xs = (S.One,) + symbols(f'x1:{numCovariates}') terms = [Mul(*[x ** deg for x, deg in zip(xs, power)]) for power in powers] return terms diff --git a/statapp/calculations.py b/statapp/calculations.py index ec8e39f..255c564 100644 --- a/statapp/calculations.py +++ b/statapp/calculations.py @@ -21,7 +21,7 @@ from dataclasses import dataclass import numpy as np import pandas as pd -from statapp._vendor.multipolyfit import multipolyfit, mk_sympy_function +from statapp._vendor.multipolyfit import multipolyfit DIRECT_LINK = 0 INDIRECT_LINK = 1 @@ -112,18 +112,13 @@ class ExtendedRegressionResult: def squaredPolynom(inputData): x = inputData[:, 1:] y = inputData[:, 0] - data = pd.DataFrame(x) result, powers, tStatistics, mse = multipolyfit(x, y, 2, full=True) betas = result[0] - res = mk_sympy_function(betas, powers) - print(data) - print(res) out = pd.DataFrame() out[0] = betas out[1] = tStatistics - return ExtendedRegressionResult( out.to_numpy(), np.float64(mse[0]), diff --git a/statapp/models/squared_polynom_model.py b/statapp/models/squared_polynom_model.py index 4875bb0..66b84f7 100644 --- a/statapp/models/squared_polynom_model.py +++ b/statapp/models/squared_polynom_model.py @@ -18,7 +18,7 @@ # along with this program. If not, see . # -from statapp._vendor.multipolyfit import get_terms +from statapp._vendor.multipolyfit import getTerms from statapp.models.linear_polynom_model import LinearPolynomModel @@ -26,4 +26,4 @@ class SquaredPolynomModel(LinearPolynomModel): powers: list def getVerticalHeader(self): - return ['c' if str(x) == '1' else str(x) for x in get_terms(self.powers)] + return ['c' if str(x) == '1' else str(x) for x in getTerms(self.powers)]