[Opus] Update to v1.2.1

third_party/opus/src/src/mlp_train.h

Index: third_party/opus/src/src/mlp_train.h
diff --git a/third_party/opus/src/src/mlp_train.h b/third_party/opus/src/src/mlp_train.h
new file mode 100644
index 0000000000000000000000000000000000000000..494041585a3f9cb643348a992145abce06fe7ebd
--- /dev/null
+++ b/third_party/opus/src/src/mlp_train.h
@@ -0,0 +1,86 @@
+/* Copyright (c) 2008-2011 Octasic Inc.
+   Written by Jean-Marc Valin */
+/*
+   Redistribution and use in source and binary forms, with or without
+   modification, are permitted provided that the following conditions
+   are met:
+
+   - Redistributions of source code must retain the above copyright
+   notice, this list of conditions and the following disclaimer.
+
+   - Redistributions in binary form must reproduce the above copyright
+   notice, this list of conditions and the following disclaimer in the
+   documentation and/or other materials provided with the distribution.
+
+   THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+   ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+   LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+   A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE FOUNDATION OR
+   CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+   EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+   PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+   PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
+   LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
+   NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+   SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+*/
+
+#ifndef _MLP_TRAIN_H_
+#define _MLP_TRAIN_H_
+
+#include <math.h>
+#include <stdlib.h>
+
+double tansig_table[501];
+static inline double tansig_double(double x)
+{
+    return 2./(1.+exp(-2.*x)) - 1.;
+}
+static inline void build_tansig_table(void)
+{
+    int i;
+    for (i=0;i<501;i++)
+        tansig_table[i] = tansig_double(.04*(i-250));
+}
+
+static inline double tansig_approx(double x)
+{
+    int i;
+    double y, dy;
+    if (x>=10)
+        return 1;
+    if (x<=-10)
+        return -1;
+    i = lrint(25*x);
+    x -= .04*i;
+    y = tansig_table[250+i];
+    dy = 1-y*y;
+    y = y + x*dy*(1 - y*x);
+    return y;
+}
+
+static inline float randn(float sd)
+{
+   float U1, U2, S, x;
+   do {
+      U1 = ((float)rand())/RAND_MAX;
+      U2 = ((float)rand())/RAND_MAX;
+      U1 = 2*U1-1;
+      U2 = 2*U2-1;
+      S = U1*U1 + U2*U2;
+   } while (S >= 1 || S == 0.0f);
+   x = sd*sqrt(-2 * log(S) / S) * U1;
+   return x;
+}
+
+
+typedef struct {
+    int layers;
+    int *topo;
+    double **weights;
+    double **best_weights;
+    double *in_rate;
+} MLPTrain;
+
+
+#endif /* _MLP_TRAIN_H_ */