wip

.pre-commit-config.yaml

@@ -16,7 +16,7 @@ repos:
     rev: v0.8.8
     hooks:
     -   id: licenseheaders
-        args: ["-t", ".copyright.tmpl", "-cy", "-f", "-d", "statapp"]
+        args: ["-t", ".copyright.tmpl", "-cy", "-f", "-d", "statapp", "-x", "statapp/_vendor/*.py"]
         pass_filenames: false
 -   repo: local
     hooks:

poetry.lock

@@ -210,6 +210,23 @@ files = [
     {file = "mccabe-0.7.0.tar.gz", hash = "sha256:348e0240c33b60bbdf4e523192ef919f28cb2c3d7d5c7794f74009290f236325"},
 ]
 
+[[package]]
+name = "mpmath"
+version = "1.3.0"
+description = "Python library for arbitrary-precision floating-point arithmetic"
+optional = false
+python-versions = "*"
+files = [
+    {file = "mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c"},
+    {file = "mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f"},
+]
+
+[package.extras]
+develop = ["codecov", "pycodestyle", "pytest (>=4.6)", "pytest-cov", "wheel"]
+docs = ["sphinx"]
+gmpy = ["gmpy2 (>=2.1.0a4)"]
+tests = ["pytest (>=4.6)"]
+
 [[package]]
 name = "nodeenv"
 version = "1.8.0"
@@ -605,6 +622,20 @@ files = [
     {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
 ]
 
+[[package]]
+name = "sympy"
+version = "1.12"
+description = "Computer algebra system (CAS) in Python"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "sympy-1.12-py3-none-any.whl", hash = "sha256:c3588cd4295d0c0f603d0f2ae780587e64e2efeedb3521e46b9bb1d08d184fa5"},
+    {file = "sympy-1.12.tar.gz", hash = "sha256:ebf595c8dac3e0fdc4152c51878b498396ec7f30e7a914d6071e674d49420fb8"},
+]
+
+[package.dependencies]
+mpmath = ">=0.19"
+
 [[package]]
 name = "tomli"
 version = "2.0.1"
@@ -771,4 +802,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8,<3.9"
-content-hash = "ff683c2a3f778cd6ad946d6aa4b1f567514f36026e07fccffdb2cd7e86778e0a"
+content-hash = "21505ce00b560ff75b732d5cb5ee983d1aff7b6e5dc919a306ac2147ea14f3ea"

pyproject.toml

@@ -17,6 +17,7 @@ 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"
+sympy = "^1.12"
 
 
 [build-system]

statapp/__main__.py

@@ -19,9 +19,12 @@
 #
 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
 
 
@@ -40,4 +43,18 @@ 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())

statapp/_vendor/multipolyfit.py

@@ -0,0 +1,116 @@
+# Copyright (c) 2023 Matthew Rocklin
+# All rights reserved.
+
+# 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.
+
+# In addition, this code has been modified by Maxim Slipenko and
+# 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
+import itertools
+
+def basis_vector(n, i):
+    """ Return an array like [0, 0, ..., 1, ..., 0, 0]
+
+    >>> from multipolyfit.core import basis_vector
+    >>> basis_vector(3, 1)
+    array([0, 1, 0])
+    >>> basis_vector(5, 4)
+    array([0, 0, 0, 0, 1])
+    """
+    x = zeros(n, dtype=int)
+    x[i] = 1
+    return x
+
+def as_tall(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):
+    """
+    Least squares multivariate polynomial fit
+
+    Fit a polynomial like ``y = a**2 + 3a - 2ab + 4b**2 - 1``
+    with many covariates a, b, c, ...
+
+    Parameters
+    ----------
+
+    xs : array_like, shape (M, k)
+         x-coordinates of the k covariates over the M sample points
+    y :  array_like, shape(M,)
+         y-coordinates of the sample points.
+    deg : int
+         Degree o fthe fitting polynomial
+    model_out : bool (defaults to True)
+         If True return a callable function
+         If False return an array of coefficients
+    powers_out : 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
+         [1, 2, 1, 0] corresponds to 1**1 * a**2 * b**1 * c**0
+
+    See Also
+    --------
+        numpy.polyfit
+
+    """
+    y = asarray(y).squeeze()
+    rows = y.shape[0]
+    xs = asarray(xs)
+    num_covariates = xs.shape[1]
+    xs = hstack((ones((xs.shape[0], 1), dtype=xs.dtype) , xs))
+
+    generators = [basis_vector(num_covariates+1, i)
+                            for i in range(num_covariates+1)]
+
+    # All combinations of degrees
+    powers = [sum(x) for x in itertools.combinations_with_replacement(generators, deg)]
+
+    # Raise data to specified degree pattern, stack in order
+    A = hstack(asarray([as_tall((xs**p).prod(1)) for p in powers]))
+
+    beta = linalg.lstsq(A, y, rcond=None)[0]
+
+    if model_out:
+        return mk_model(beta, powers)
+
+    if powers_out:
+        return beta, powers
+    return beta
+
+def mk_model(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
+    """
+    # 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)
+        xs = asarray((1,) + args)
+        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)
+    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]) - 1
+    xs = (S.One,) + symbols('x0:%d' % num_covariates)
+
+    terms = [Mul(*[x ** deg for x, deg in zip(xs, power)]) for power in powers]
+    return terms

statapp/calculations.py

@@ -21,6 +21,7 @@ from dataclasses import dataclass
 
 import numpy as np
 import pandas as pd
+from statapp._vendor.multipolyfit import multipolyfit, mk_sympy_function
 
 DIRECT_LINK = 0
 INDIRECT_LINK = 1
@@ -92,3 +93,14 @@ def linearPolynom(inputData) -> LinearPolynomResult:
         out.to_numpy(),
         np.float64(mse[0])
     )
+
+def squaredPolynom(inputData) -> LinearPolynomResult:
+    x = inputData[:, 1:]
+    y = inputData[:, 0]
+    data = pd.DataFrame(x)
+    betas, powers = multipolyfit(x, y, 2, powers_out=True)
+    res = mk_sympy_function(betas, powers)
+    print(data)
+    print(res)
+
+    return powers