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#pragma once
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/*
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* Copyright (C) 2024 Brett Terpstra
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*
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* This program is free software: you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* This program is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with this program. If not, see <https://www.gnu.org/licenses/>.
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*/
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#ifndef COSC_4P80_ASSIGNMENT_1_FWD_DECL_H
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#define COSC_4P80_ASSIGNMENT_1_FWD_DECL_H
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#include <blt/math/matrix.h>
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#include <blt/math/log_util.h>
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namespace a1
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{
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void test_math()
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{
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blt::generalized_matrix<float, 1, 4> input{1, -1, -1, 1};
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blt::generalized_matrix<float, 1, 3> output{1, 1, 1};
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blt::generalized_matrix<float, 4, 3> expected{
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blt::vec4{1, -1, -1, 1},
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blt::vec4{1, -1, -1, 1},
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blt::vec4{1, -1, -1, 1}
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};
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auto w_matrix = input.transpose() * output;
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BLT_ASSERT(w_matrix == expected && "MATH MATRIX FAILURE");
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blt::vec4 one{5, 1, 3, 0};
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blt::vec4 two{9, -5, -8, 3};
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blt::generalized_matrix<float, 1, 4> g1{5, 1, 3, 0};
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blt::generalized_matrix<float, 1, 4> g2{9, -5, -8, 3};
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BLT_ASSERT(g1 * g2.transpose() == blt::vec4::dot(one, two) && "MATH DOT FAILURE");
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}
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enum class recall_error_t
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{
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// failed to predict input
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INPUT_FAILURE,
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// failed to predict output
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OUTPUT_FAILURE
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};
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}
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#endif //COSC_4P80_ASSIGNMENT_1_FWD_DECL_H
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2
lib/blt
2
lib/blt
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@ -1 +1 @@
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Subproject commit b5ea7a1e1500dc695490c730dcedbc93dae3ba73
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Subproject commit 7300f895bb8c1e7f1c5a96866d466126ee861281
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69
src/main.cpp
69
src/main.cpp
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#include <blt/math/log_util.h>
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#include <blt/math/log_util.h>
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#include "blt/std/assert.h"
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#include "blt/std/assert.h"
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#include <blt/format/boxing.h>
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#include <blt/format/boxing.h>
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#include <fwd_decl.h>
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void test_math()
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{
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blt::generalized_matrix<float, 1, 4> input{1, -1, -1, 1};
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blt::generalized_matrix<float, 1, 3> output{1, 1, 1};
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blt::generalized_matrix<float, 4, 3> expected{
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blt::vec4{1, -1, -1, 1},
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blt::vec4{1, -1, -1, 1},
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blt::vec4{1, -1, -1, 1}
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};
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auto w_matrix = input.transpose() * output;
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BLT_ASSERT(w_matrix == expected && "MATH FAILURE");
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}
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constexpr blt::u32 num_values = 4;
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constexpr blt::u32 num_values = 4;
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constexpr blt::u32 input_count = 5;
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constexpr blt::u32 input_count = 5;
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@ -42,67 +29,43 @@ output_t output_2{1, -1, -1, -1};
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output_t output_3{-1, -1, 1, 1};
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output_t output_3{-1, -1, 1, 1};
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output_t output_4{-1, 1, 1, -1};
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output_t output_4{-1, 1, 1, -1};
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weight_t weight_1 = input_1.transpose() * output_1;
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const weight_t weight_1 = input_1.transpose() * output_1;
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weight_t weight_2 = input_2.transpose() * output_2;
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const weight_t weight_2 = input_2.transpose() * output_2;
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weight_t weight_3 = input_3.transpose() * output_3;
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const weight_t weight_3 = input_3.transpose() * output_3;
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weight_t weight_4 = input_4.transpose() * output_4;
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const weight_t weight_4 = input_4.transpose() * output_4;
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auto inputs = std::array{input_1, input_2, input_3, input_4};
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auto starting_inputs = std::array{input_1, input_2, input_3, input_4};
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auto outputs = std::array{output_1, output_2, output_3, output_4};
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auto starting_outputs = std::array{output_1, output_2, output_3, output_4};
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auto weight_total_a = weight_1 + weight_2 + weight_3;
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const auto weight_total_a = weight_1 + weight_2 + weight_3;
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auto weight_total_c = weight_total_a + weight_4;
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const auto weight_total_c = weight_total_a + weight_4;
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crosstalk_t crosstalk_values{};
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crosstalk_t crosstalk_values{};
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template<typename T, blt::u32 rows, blt::u32 columns>
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template<typename T, blt::u32 rows, blt::u32 columns>
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blt::generalized_matrix<T, rows, columns> normalize(const blt::generalized_matrix<T, rows, columns>& in)
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blt::generalized_matrix<T, rows, columns> threshold(const blt::generalized_matrix<T, rows, columns>& y, const blt::generalized_matrix<T, rows, columns>& base)
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{
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{
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blt::generalized_matrix<T, rows, columns> result;
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blt::generalized_matrix<T, rows, columns> result;
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for (blt::u32 i = 0; i < columns; i++)
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for (blt::u32 i = 0; i < columns; i++)
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{
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{
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for (blt::u32 j = 0; j < rows; j++)
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for (blt::u32 j = 0; j < rows; j++)
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result[i][j] = in[i][j] >= 0 ? 1 : -1;
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result[i][j] = y[i][j] > 1 ? 1 : (y[i][j] < -1 ? -1 : base[i][j]);
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}
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}
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return result;
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return result;
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}
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}
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auto calculate_recall()
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std::pair<input_t, output_t> run_step(const weight_t& associated_weights, const input_t& input, const output_t & output)
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{
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{
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output_t output_recall = input * associated_weights;
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}
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input_t input_recall = output * associated_weights.transpose();
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void test_recall(blt::size_t index, const weight_t& associated_weights)
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{
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auto& input = inputs[index];
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auto& output = outputs[index];
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auto output_recall = normalize(input * associated_weights);
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return std::pair{threshold(input_recall, input), threshold(output_recall, output)};
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auto input_recall = normalize(output * associated_weights.transpose());
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if (output_recall != output)
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{
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BLT_ERROR_STREAM << "Output '" << index + 1 << "' recalled failed!" << '\n';
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BLT_WARN_STREAM << "\t- Found: " << output_recall.vec_from_column_row() << '\n';
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BLT_WARN_STREAM << "\t- Expected: " << output.vec_from_column_row() << '\n';
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} else
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BLT_INFO("Output '%ld' recall passed!", index + 1);
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if (input_recall != input)
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{
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BLT_ERROR_STREAM << "Input '" << index + 1 << "' recalled failed!" << "\n";
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BLT_WARN_STREAM << "\t- Found: " << input_recall.vec_from_column_row() << '\n';
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BLT_WARN_STREAM << "\t- Expected: " << input.vec_from_column_row() << '\n';
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} else
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BLT_INFO("Input '%ld' recall passed!", index + 1);
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}
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}
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void part_a()
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void part_a()
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{
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{
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blt::log_box_t box(BLT_TRACE_STREAM, "Part A", 8);
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blt::log_box_t box(BLT_TRACE_STREAM, "Part A", 8);
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test_recall(0, weight_total_a);
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test_recall(1, weight_total_a);
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test_recall(2, weight_total_a);
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}
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}
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void part_b()
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void part_b()
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