From fce98b683c6b8cb886d612af900ffde61152686c Mon Sep 17 00:00:00 2001
From: Maxim Slipenko <no-reply@maxim.slipenko.com>
Date: Thu, 22 Feb 2024 15:01:30 +0300
Subject: [PATCH] =?UTF-8?q?fix:=20=D0=B8=D1=81=D0=BF=D1=80=D0=B0=D0=B2?=
=?UTF-8?q?=D0=BB=D0=B5=D0=BD=20prediction?=
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---
statapp/calculations.py | 13 +++++++------
1 file changed, 7 insertions(+), 6 deletions(-)
diff --git a/statapp/calculations.py b/statapp/calculations.py
index 0429549..a0f9064 100644
--- a/statapp/calculations.py
+++ b/statapp/calculations.py
@@ -113,8 +113,7 @@ def linearPolynom(data):
monomials = ['c'] + ['x' + str(i) for i in range(1, x.shape[1] + 1)]
residualVariance = np.var(residuals, ddof=k)
- scaledResidualVariance = (residualVariance /
- np.mean(residuals) ** 2) if np.mean(residuals) != 0 else np.nan
+ scaledResidualVariance = residualVariance / (n - k)
paramsAndTStats = np.vstack((params, tStats)).T
@@ -163,9 +162,7 @@ def squaredPolynom(data):
monomials = [monomial.replace(' ', '*') for monomial in monomials]
residualVariance = np.var(residuals, ddof=k)
- scaledResidualVariance = (
- residualVariance / np.mean(residuals) ** 2
- ) if np.mean(residuals) != 0 else np.nan
+ scaledResidualVariance = residualVariance / (n - k)
paramsAndTStats = np.vstack((params, tStats)).T
@@ -194,8 +191,12 @@ def prediction(inputData, result: RegressionResult):
results = []
+ numVars = inputs.shape[1]
+ symbolsStr = ' '.join([f'x{i}' for i in range(1, numVars + 1)])
+ symbols = sp.symbols(symbolsStr)
+
for y, xValues in zip(outputs, inputs):
- subsDict = dict(zip(expr.free_symbols, xValues))
+ subsDict = dict(zip(symbols, xValues))
predictedResult = expr.subs(subsDict)
difference = predictedResult - y