'update'

[aggexp.git] / pkg / src / ml.predict_noNA.c

diff --git a/pkg/src/ml.predict_noNA.c b/pkg/src/ml.predict_noNA.c
new file mode 100644 (file)
index 0000000..03a5355
--- /dev/null
+++ b/pkg/src/ml.predict_noNA.c
@@ -0,0 +1,64 @@
+#include <math.h>
+#include <stdlib.h>
+
+void ml_predict_noNA(double* X, double* Y, int* n_, int* K_, double* alpha_, int* grad_, double* weight)
+{
+       int K = *K_;
+       int n = *n_;
+       double alpha = *alpha_;
+       int grad = *grad_;
+
+       //at least two experts to combine: various inits
+       double initWeight = 1. / K;
+       for (int i=0; i<K; i++)
+               weight[i] = initWeight;
+       double* error = (double*)malloc(K*sizeof(double));
+       double* cumDeltaError = (double*)calloc(K, sizeof(double));
+       double* regret = (double*)calloc(K, sizeof(double));
+
+       //start main loop
+       for (int t=0; t<n; t++ < n)
+       {
+               if (grad)
+               {
+                       double hatY = 0.;
+                       for (int i=0; i<K; i++)
+                               hatY += X[t*K+i] * weight[i];
+                       for (int i=0; i<K; i++)
+                               error[i] = 2. * (hatY - Y[t]) * X[t*K+i];
+               }
+               else
+               {
+                       for (int i=0; i<K; i++)
+                       {
+                               double delta = X[t*K+i] - Y[t];
+                               error[i] = delta * delta;
+                       }
+               }
+
+               double hatError = 0.;
+               for (int i=0; i<K; i++)
+                       hatError += error[i] * weight[i];
+               for (int i=0; i<K; i++)
+               {
+                       double deltaError = hatError - error[i];
+                       cumDeltaError[i] += deltaError * deltaError;
+                       regret[i] += deltaError;
+                       double eta = 1. / (1. + cumDeltaError[i]);
+                       weight[i] = regret[i] > 0. ? eta * regret[i] : 0.;
+               }
+
+               double sumWeight = 0.0;
+               for (int i=0; i<K; i++)
+                       sumWeight += weight[i];
+               for (int i=0; i<K; i++)
+                       weight[i] /= sumWeight;
+               //redistribute weights if alpha > 0 (all weights are 0 or more, sum > 0)
+               for (int i=0; i<K; i++)
+                       weight[i] = (1. - alpha) * weight[i] + alpha/K;
+       }
+
+       free(error);
+       free(cumDeltaError);
+       free(regret);
+}