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use STL's inner_product

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This commit is contained in:
Krishna Vedala 2020-05-31 06:41:26 -04:00
parent 1c05a58cea
commit f94a330594
1 changed files with 4 additions and 2 deletions
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6
machine_learning/adaline_learning.cpp
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@ -27,6 +27,7 @@
#include <ctime>
#include <iostream>
#include <vector>
#include <numeric>
#define MAX_ITER 500 // INT_MAX ///< Maximum number of iterations to learn
@ -81,7 +82,8 @@ class adaline {
double y = weights.back(); // assign bias value
for (int i = 0; i < x.size(); i++) y += x[i] * weights[i];
// for (int i = 0; i < x.size(); i++) y += x[i] * weights[i];
y = std::inner_product(x.begin(), x.end(), weights.begin(), y);
return y >= 0 ? 1 : -1; // quantizer: apply ADALINE threshold function
}
@ -229,7 +231,7 @@ void test2(double eta = 0.01) {
Y[i] = (x0 + x1) > -1 ? 1 : -1;
}
std::cout << "------- Test 1 -------" << std::endl;
std::cout << "------- Test 2 -------" << std::endl;
std::cout << "Model before fit: " << ada << std::endl;
ada.fit(X, Y);