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Krishna Vedala 2020-05-28 23:11:39 -04:00
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commit 2e0a50b6f5
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1 changed files with 7 additions and 3 deletions
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computer_oriented_statistical_methods/ordinary_least_squares_regressor.cpp
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@ -1,11 +1,13 @@
/** /**
* @file * @file
* \brief Linear regression example using [Ordinary least
* squares](https://en.wikipedia.org/wiki/Ordinary_least_squares)
* *
* Program that gets the number of data samples and number of features per * Program that gets the number of data samples and number of features per
* sample along with output per sample. It applies OLS regression to compute * sample along with output per sample. It applies OLS regression to compute
* the regression output for additional test data samples. * the regression output for additional test data samples.
*/ */
#include <iomanip> #include <iomanip> // for print formatting
#include <iostream> #include <iostream>
#include <vector> #include <vector>
@ -52,7 +54,8 @@ inline bool is_square(std::vector<std::vector<T>> const &A) {
// Assuming A is square matrix // Assuming A is square matrix
size_t N = A.size(); size_t N = A.size();
for (size_t i = 0; i < N; i++) for (size_t i = 0; i < N; i++)
if (A[i].size() != N) return false; if (A[i].size() != N)
return false;
return true; return true;
} }
@ -265,7 +268,8 @@ std::vector<std::vector<float>> get_inverse(
inverse[row] = inverse[row] / divisor; inverse[row] = inverse[row] / divisor;
// Row transformations // Row transformations
for (size_t row2 = 0; row2 < N; row2++) { for (size_t row2 = 0; row2 < N; row2++) {
if (row2 == row) continue; if (row2 == row)
continue;
float factor = temp[row2][row]; float factor = temp[row2][row];
temp[row2] = temp[row2] - factor * temp[row]; temp[row2] = temp[row2] - factor * temp[row];
inverse[row2] = inverse[row2] - factor * inverse[row]; inverse[row2] = inverse[row2] - factor * inverse[row];