Remove std:: from powf

src/machine_learning/loss_functions/losses.h

@@ -36,7 +36,7 @@ namespace losses {
     size_t n = y.size();
     double mse = 0.0;
     for(size_t i = 0; i<n; i++) {
-        mse += std::powf(y[i] - y_hat[i], 2);
+        mse += powf(y[i] - y_hat[i], 2);
     }
     return mse / double(n);
   }

src/machine_learning/loss_functions/mean_squared_error.h

@@ -0,0 +1,61 @@
+#ifndef MEAN_SQUARED_ERROR_H
+#define MEAN_SQUARED_ERROR_H
+
+#ifdef __cplusplus
+#include <iostream>
+#include <algorithm>
+#include <vector>
+#include <cmath>
+#include <cassert>
+#endif
+
+
+namespace losses {
+  float mean_squared_error(std::vector<float> const& y, std::vector<float> const& y_hat) {
+    assert(y.size() == y_hat.size());
+    size_t n = y.size();
+    float mse = 0.0;
+    for(size_t i = 0; i<n; i++) {
+        mse += std::powf(y[i] - y_hat[i], 2);
+    }
+    return mse / float(n);
+  }
+
+  float root_mean_squared_error(std::vector<float> const& y, std::vector<float> const& y_hat) {
+    return std::sqrt(mean_squared_error(y, y_hat));
+  }
+
+  float mean_absolute_error(std::vector<float> const& y, std::vector<float> const& y_hat) {
+    assert(y.size() == y_hat.size());
+    size_t n = y.size();
+    float mae = 0.0;
+    for(size_t i = 0; i<n; i++) {
+        mae += std::abs(y[i] - y_hat[i]);
+    }
+    return mae / float(n);
+  }
+
+  float binary_crossentropy_loss(std::vector<float> const& y, std::vector<float> const& y_hat) {
+    assert(y.size() == y_hat.size());
+    size_t n = y.size();
+    float bce = 0.0;
+    for(size_t i = 0; i<n; i++) {
+        bce += (y[i]*std::log(y_hat[i]) + (1-y[i])*std::log(1 - y_hat[i]));
+    }
+    return -(1/float(n))*bce;
+  }
+
+  float crossentropy_loss(std::vector<float> const& y, std::vector<float> const& y_hat) {
+    assert(y.size() == y_hat.size());
+    size_t n = y.size();
+    float ce = 0.0;
+    for(size_t i = 0; i<n; i++) {
+        ce += y[i]*std::log(y_hat[i]);
+    }
+    return -(1/float(n))*ce;
+  }
+}
+
+
+#endif
+