1 files changed, 147 insertions, 0 deletions
diff --git a/pix_linNN/LinNeuralNet.cpp b/pix_linNN/LinNeuralNet.cpp
new file mode 100755
index 0000000..87318a0
--- /dev/null
+++ b/pix_linNN/LinNeuralNet.cpp
@@ -0,0 +1,147 @@
+/////////////////////////////////////////////////////////////////////////////
+//
+// class LinNeuralNet
+//
+//   source file
+//
+//   Copyright (c) 2004 Georg Holzmann <grh@gmx.at>
+//
+//   For information on usage and redistribution, and for a DISCLAIMER OF ALL
+//   WARRANTIES, see the file, "GEM.LICENSE.TERMS" in this distribution.
+//
+/////////////////////////////////////////////////////////////////////////////
+
+#include "LinNeuralNet.h"
+
+//--------------------------------------------------
+/* Constructor
+ */
+LinNeuralNet::LinNeuralNet(int netsize) : learn_rate_(0), range_(1), IW_(NULL), b1_(0)
+{
+  // set random seed:
+  srand( (unsigned)time(NULL) );
+
+  netsize_ = (netsize<1) ? 1 : netsize;
+}
+
+//--------------------------------------------------
+/* Destructor
+ */
+LinNeuralNet::~LinNeuralNet()
+{
+  if(IW_)
+    delete[] IW_;
+}
+
+//--------------------------------------------------
+/* creates a new IW-matrix (size: netsize_) and 
+ * b1-vector
+ * ATTENTION: if they exist they'll be deleted
+ */
+bool LinNeuralNet::createNeurons()
+{
+  // delete if they exist
+  if(IW_)
+    delete[] IW_;
+
+  IW_ = new float[netsize_];
+  if(!IW_)
+    return false;
+
+  return true;
+}
+
+//--------------------------------------------------
+/* inits the weight matrix and the bias vector of
+ * the network with random values between [min|max]
+ */
+bool LinNeuralNet::initNetworkRand(const int &min, const int &max)
+{
+  if(!IW_)
+    return false;
+
+  // make randomvalue between 0 and 1
+  // then map it to the bounds
+  b1_ = ((float)rand()/(float)RAND_MAX)*(max-min) + min;
+
+  for(int i=0; i<netsize_; i++)
+    {
+      IW_[i] = ((float)rand()/(float)RAND_MAX)*(max-min) + min;
+    }
+
+  return true;
+}
+
+//--------------------------------------------------
+/* inits the net with a given weight matrix and bias
+ * (makes a deep copy)
+ * ATTENTION: the dimension of IW-pointer must be the same
+ *            as the netsize !!!
+ * returns false if there's a failure
+ */
+bool LinNeuralNet::initNetwork(const float *IW, float b1)
+{
+  if(!IW_)
+    return false;
+
+  b1_ = b1;
+
+  for(int i=0; i<netsize_; i++)
+      IW_[i] = IW[i];
+
+  return true;
+}
+
+//--------------------------------------------------
+/* calculates the output with the current IW, b1 values
+ * ATTENTION: the array input_data must be in the same
+ *            size as netsize_
+ */
+float LinNeuralNet::calculateNet(float *input_data)
+{
+  if(!IW_)
+    return 0;
+
+  float output = 0;
+
+  // multiply the inputs with the weight matrix IW
+  // and add the bias vector b1
+  for(int i=0; i<netsize_; i++)
+      output += input_data[i] * IW_[i];
+  
+  // map input values to the range
+  output /= range_;
+  
+  return (output+b1_);
+}
+
+//--------------------------------------------------
+/* this method trains the network:
+ * input_data is, as above, the input data, output_data is the 
+ * output of the current net with input_data (output_data is not
+ * calculated in that method !), target_output is the desired
+ * output data
+ * (this is the LMS-algorithm to train linear neural networks)
+ * ATTENTION: the array input_data must be in the same
+ *            size as netsize_
+ */
+bool LinNeuralNet::trainNet(float *input_data, const float &output_data, 
+			    const float &target_output)
+{
+  if(!IW_)
+    return false;
+
+  // this is the LMS-algorithm to train linear
+  // neural networks
+  
+  // calculate the error signal:
+  float error = (target_output - output_data);
+
+  // now change the weights the bias
+  for(int i=0; i<netsize_; i++)
+    IW_[i] += 2 * learn_rate_ * error * (input_data[i]/range_);
+
+  b1_ += 2 * learn_rate_ * error; 
+
+  return true;
+}