├── .idea
    ├── .name
    ├── vcs.xml
    ├── modules.xml
    ├── ML.iml
    ├── misc.xml
    └── workspace.xml
├── CMakeLists.txt
├── Utils.h
├── Utils.cpp
├── LinearRegression.h
├── LinearRegression.cpp
└── main.cpp


/.idea/.name:
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1 | ML


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/.idea/vcs.xml:
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1 | <?xml version="1.0" encoding="UTF-8"?>
2 | <project version="4">
3 |   <component name="VcsDirectoryMappings">
4 |     <mapping directory="" vcs="" />
5 |   </component>
6 | </project>


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/CMakeLists.txt:
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1 | cmake_minimum_required(VERSION 3.1)
2 | project(ML)
3 | 
4 | set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
5 | 
6 | set(SOURCE_FILES main.cpp)
7 | add_executable(ML ${SOURCE_FILES} LinearRegression.cpp LinearRegression.h Utils.cpp Utils.h)
8 | 


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/.idea/modules.xml:
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1 | <?xml version="1.0" encoding="UTF-8"?>
2 | <project version="4">
3 |   <component name="ProjectModuleManager">
4 |     <modules>
5 |       <module fileurl="file://$PROJECT_DIR$/.idea/ML.iml" filepath="$PROJECT_DIR$/.idea/ML.iml" />
6 |     </modules>
7 |   </component>
8 | </project>


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/.idea/ML.iml:
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1 | <?xml version="1.0" encoding="UTF-8"?>
2 | <module type="CPP_MODULE" version="4">
3 |   <component name="NewModuleRootManager">
4 |     <content url="file://$MODULE_DIR$" />
5 |     <orderEntry type="inheritedJdk" />
6 |     <orderEntry type="sourceFolder" forTests="false" />
7 |   </component>
8 | </module>


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/Utils.h:
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 1 | #ifndef ML_UTILS_H
 2 | #define ML_UTILS_H
 3 | 
 4 | #include <vector>
 5 | 
 6 | class Utils {
 7 | 
 8 | public:
 9 |     static double array_sum(double arr[], int len);
10 | 
11 |     static double *array_pow(double arr[], int len, int power);
12 | 
13 |     static double *array_multiplication(double arr1[], double arr2[], int len);
14 | 
15 |     static double *array_diff(double arr1[], double arr2[], int len);
16 | 
17 | };
18 | 
19 | #endif //ML_UTILS_H
20 | 


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/.idea/misc.xml:
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 1 | <?xml version="1.0" encoding="UTF-8"?>
 2 | <project version="4">
 3 |   <component name="CMakeWorkspace" PROJECT_DIR="$PROJECT_DIR$" />
 4 |   <component name="ProjectLevelVcsManager" settingsEditedManually="false">
 5 |     <OptionsSetting value="true" id="Add" />
 6 |     <OptionsSetting value="true" id="Remove" />
 7 |     <OptionsSetting value="true" id="Checkout" />
 8 |     <OptionsSetting value="true" id="Update" />
 9 |     <OptionsSetting value="true" id="Status" />
10 |     <OptionsSetting value="true" id="Edit" />
11 |     <ConfirmationsSetting value="0" id="Add" />
12 |     <ConfirmationsSetting value="0" id="Remove" />
13 |   </component>
14 |   <component name="ProjectRootManager" version="2" />
15 | </project>


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/Utils.cpp:
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 1 | #include <cmath>
 2 | #include "Utils.h"
 3 | 
 4 | double Utils::array_sum(double arr[], int len) {
 5 |     double s = 0;
 6 | 
 7 |     for (int i = 0; i < len; ++i) {
 8 |         s += arr[i];
 9 |     }
10 | 
11 |     return s;
12 | }
13 | 
14 | double *Utils::array_pow(double arr[], int len, int power) {
15 |     double *arr2 = new double[len];
16 | 
17 |     for (int i = 0; i < len; ++i) {
18 |         arr2[i] = pow(arr[i], power);
19 |     }
20 | 
21 |     return arr2;
22 | }
23 | 
24 | double *Utils::array_multiplication(double arr1[], double arr2[], int len) {
25 |     double *arr = new double[len];
26 | 
27 |     for (int i = 0; i < len; ++i) {
28 |         arr[i] = arr1[i] * arr2[i];
29 |     }
30 | 
31 |     return arr;
32 | }
33 | 
34 | double *Utils::array_diff(double arr1[], double arr2[], int len) {
35 |     double *arr = new double[len];
36 | 
37 |     for (int i = 0; i < len; ++i) {
38 |         arr[i] = arr1[i] - arr2[i];
39 |     }
40 | 
41 |     return arr;
42 | }
43 | 


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/LinearRegression.h:
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 1 | #ifndef ML_LINEARREGRESSION_H
 2 | #define ML_LINEARREGRESSION_H
 3 | 
 4 | class LinearRegression {
 5 | 
 6 | public:
 7 | 
 8 |     // First feature
 9 |     double *x;
10 | 
11 |     // Target feature
12 |     double *y;
13 | 
14 |     // Number of training examples
15 |     int m;
16 | 
17 |     // The theta coefficients
18 |     double *theta;
19 | 
20 |     /**
21 |      * Create a new instance from the given data set.
22 |      */
23 |     LinearRegression(double x[], double y[], int m);
24 | 
25 |     /**
26 |      * Train the model with the supplied parameters.
27 |      *
28 |      * @param alpha         The learning rate, e.g. 0.01.
29 |      * @param iterations    The number of gradient descent steps to do.
30 |      */
31 |     void train(double alpha, int iterations);
32 | 
33 |     /**
34 |      * Try to predict y, given an x.
35 |      */
36 |     double predict(double x);
37 | 
38 | private:
39 | 
40 |     /**
41 |      * Compute the cost J.
42 |      */
43 |     static double compute_cost(double x[], double y[], double theta[], int m);
44 | 
45 |     /**
46 |      * Compute the hypothesis.
47 |      */
48 |     static double h(double x, double theta[]);
49 | 
50 |     /**
51 |      * Calculate the target feature from the other ones.
52 |      */
53 |     static double *calculate_predictions(double x[], double theta[], int m);
54 | 
55 |     /**
56 |      * Performs gradient descent to learn theta by taking num_items gradient steps with learning rate alpha.
57 |      */
58 |     static double *gradient_descent(double x[], double y[], double alpha, int iters, double *J, int m);
59 | 
60 | };
61 | 
62 | 
63 | #endif //ML_LINEARREGRESSION_H
64 | 


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/LinearRegression.cpp:
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 1 | #include <iostream>
 2 | #include "LinearRegression.h"
 3 | #include "Utils.h"
 4 | 
 5 | using namespace std;
 6 | 
 7 | LinearRegression::LinearRegression(double x[], double y[], int m) {
 8 |     this->x = x;
 9 |     this->y = y;
10 |     this->m = m;
11 | }
12 | 
13 | void LinearRegression::train(double alpha, int iterations) {
14 |     double *J = new double[iterations];
15 |     this->theta = gradient_descent(x, y, alpha, iterations, J, m);
16 | 
17 |     cout << "J = ";
18 |     for (int i = 0; i < iterations; ++i) {
19 |         cout << J[i] << ' ';
20 |     }
21 |     cout << endl << "Theta: " << theta[0] << " " << theta[1] << endl;
22 | }
23 | 
24 | double LinearRegression::predict(double x) {
25 |     return h(x, theta);
26 | }
27 | 
28 | double LinearRegression::compute_cost(double x[], double y[], double theta[], int m) {
29 |     double *predictions = calculate_predictions(x, theta, m);
30 |     double *diff = Utils::array_diff(predictions, y, m);
31 |     double *sq_errors = Utils::array_pow(diff, m, 2);
32 |     return (1.0 / (2 * m)) * Utils::array_sum(sq_errors, m);
33 | }
34 | 
35 | double LinearRegression::h(double x, double theta[]) {
36 |     return theta[0] + theta[1] * x;
37 | }
38 | 
39 | double *LinearRegression::calculate_predictions(double x[], double theta[], int m) {
40 |     double *predictions = new double[m];
41 | 
42 |     // calculate h for each training example
43 |     for (int i = 0; i < m; ++i) {
44 |         predictions[i] = h(x[i], theta);
45 |     }
46 | 
47 |     return predictions;
48 | }
49 | 
50 | double *LinearRegression::gradient_descent(double x[], double y[], double alpha, int iters, double *J, int m) {
51 |     double *theta = new double[2];
52 |     theta[0] = 1;
53 |     theta[1] = 1;
54 | 
55 |     for (int i = 0; i < iters; ++i) {
56 |         double *predictions = calculate_predictions(x, theta, m);
57 |         double *diff = Utils::array_diff(predictions, y, m);
58 | 
59 |         double *errors_x1 = diff;
60 |         double *errors_x2 = Utils::array_multiplication(diff, x, m);
61 | 
62 |         theta[0] = theta[0] - alpha * (1.0 / m) * Utils::array_sum(errors_x1, m);
63 |         theta[1] = theta[1] - alpha * (1.0 / m) * Utils::array_sum(errors_x2, m);
64 | 
65 |         J[i] = compute_cost(x, y, theta, m);
66 |     }
67 | 
68 |     return theta;
69 | }
70 | 


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/main.cpp:
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  1 | #include <iostream>
  2 | #include "LinearRegression.h"
  3 | 
  4 | using namespace std;
  5 | 
  6 | void chirps_vs_temp() {
  7 |     double chirps[] = {20, 16, 20, 18, 17, 16, 15, 17, 15, 16, 15, 17, 16, 17, 14};
  8 |     double temperatures[] = {89, 72, 93, 84, 81, 75, 70, 82, 69, 83, 80, 83, 81, 84, 76};
  9 | 
 10 |     LinearRegression lr(chirps, temperatures, 15);
 11 | 
 12 |     cout << "Enter learning rate alpha (default: 0.01): ";
 13 |     double alpha;
 14 |     cin >> alpha;
 15 | 
 16 |     cout << "Enter number of iterations (default: 1500): ";
 17 |     int iterations;
 18 |     cin >> iterations;
 19 | 
 20 |     cout << "Training model..." << endl;
 21 |     lr.train(alpha, iterations);
 22 | 
 23 |     cout << "Model has been trained, enter number of chirps: ";
 24 |     double x;
 25 |     cin >> x;
 26 | 
 27 |     double temperature = lr.predict(x);
 28 |     cout << "Estimated temperature: " << temperature << " F" << endl;
 29 | }
 30 | 
 31 | void sand_vs_slope() {
 32 |     double diameter[] = {0.17, 0.19, 0.22, 0.235, 0.235, 0.3, 0.35, 0.42, 0.85};
 33 |     double slope[] = {0.63, 0.7, 0.82, 0.88, 1.15, 1.5, 4.4, 7.3, 11.3};
 34 | 
 35 |     LinearRegression lr(diameter, slope, 9);
 36 | 
 37 |     cout << "Enter learning rate alpha (default: 0.01): ";
 38 |     double alpha;
 39 |     cin >> alpha;
 40 | 
 41 |     cout << "Enter number of iterations (default: 1500): ";
 42 |     int iterations;
 43 |     cin >> iterations;
 44 | 
 45 |     cout << "Training model..." << endl;
 46 |     lr.train(alpha, iterations);
 47 | 
 48 |     cout << "Model has been trained, enter diameter: ";
 49 |     double x;
 50 |     cin >> x;
 51 | 
 52 |     double temperature = lr.predict(x);
 53 |     cout << "Estimated slope: " << temperature << endl;
 54 | }
 55 | 
 56 | void fires_vs_theft() {
 57 |     double fires[] = {6.2, 9.5, 10.5, 7.7, 8.6, 34.1, 11, 6.9, 7.3, 15.1, 29.1, 2.2, 5.7, 2, 2.5, 4, 5.4, 2.2, 7.2,
 58 |                       15.1, 16.5, 18.4, 36.2, 39.7, 18.5, 23.3, 12.2, 5.6, 21.8, 21.6, 9, 3.6, 5, 28.6, 17.4, 11.3, 3.4,
 59 |                       11.9, 10.5, 10.7, 10.8, 4.8};
 60 |     double theft[] = {29, 44, 36, 37, 53, 68, 75, 18, 31, 25, 34, 14, 11, 11, 22, 16, 27, 9, 29, 30, 40, 32, 41, 147,
 61 |                       22, 29, 46, 23, 4, 31, 39, 15, 32, 27, 32, 34, 17, 46, 42, 43, 34, 19};
 62 | 
 63 |     LinearRegression lr(fires, theft, 42);
 64 | 
 65 |     cout << "Enter learning rate alpha (default: 0.01): ";
 66 |     double alpha;
 67 |     cin >> alpha;
 68 | 
 69 |     cout << "Enter number of iterations (default: 1500): ";
 70 |     int iterations;
 71 |     cin >> iterations;
 72 | 
 73 |     cout << "Training model..." << endl;
 74 |     lr.train(alpha, iterations);
 75 | 
 76 |     cout << "Model has been trained, enter fires: ";
 77 |     double x;
 78 |     cin >> x;
 79 | 
 80 |     double temperature = lr.predict(x);
 81 |     cout << "Estimated theft: " << temperature << endl;
 82 | }
 83 | 
 84 | int main() {
 85 |     while (1) {
 86 |         cout << "Choose example: " << endl;
 87 |         cout << "0: Exit" << endl;
 88 |         cout << "1. Cricket chirps/sec vs temperature in degrees fahrenheit" << endl;
 89 |         cout << "2. Diameter (mm) of sand granules vs slope on natural beach (degrees)" << endl;
 90 |         cout << "3. Fires per 1000 houses vs thefts per 1000 population in Chicago" << endl;
 91 | 
 92 |         int opt;
 93 |         cin >> opt;
 94 | 
 95 |         if (!opt) {
 96 |             break;
 97 |         }
 98 | 
 99 |         cout << endl;
100 | 
101 |         switch (opt) {
102 |             case 1:
103 |                 chirps_vs_temp();
104 |                 break;
105 | 
106 |             case 2:
107 |                 sand_vs_slope();
108 |                 break;
109 | 
110 |             case 3:
111 |                 fires_vs_theft();
112 |                 break;
113 | 
114 |             default:
115 |                 break;
116 |         }
117 | 
118 |         cout << endl << endl;
119 |     }
120 | 
121 |     return 0;
122 | }
123 | 


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  1 | <?xml version="1.0" encoding="UTF-8"?>
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