--- a/kernel_regression.py	Sat Jul 19 17:57:46 2008 -0400
+++ b/kernel_regression.py	Tue Jul 22 15:20:25 2008 -0400
@@ -82,9 +82,11 @@
        - 'squared_error' (optionally produced by learned model if 'target' is provided)
           = example-wise squared error
     """
-    def __init__(self, kernel=None, L2_regularizer=0, gamma=1):
+    def __init__(self, kernel=None, L2_regularizer=0, gamma=1, use_bias=False):
+        # THE VERSION WITH BIAS DOES NOT SEEM RIGHT
         self.kernel = kernel
         self.L2_regularizer=L2_regularizer
+        self.use_bias=use_bias
         self.gamma = gamma # until we fix things, the kernel type is fixed, Gaussian
         self.equations = KernelRegressionEquations()
 
@@ -93,19 +95,22 @@
         first_example = trainset[0]
         n_inputs = first_example['input'].size
         n_outputs = first_example['target'].size
-        M = numpy.zeros((n_examples+1,n_examples+1))
-        Y = numpy.zeros((n_examples+1,n_outputs))
+        b1=1 if self.use_bias else 0
+        M = numpy.zeros((n_examples+b1,n_examples+b1))
+        Y = numpy.zeros((n_examples+b1,n_outputs))
         for i in xrange(n_examples):
-            M[i+1,i+1]=self.L2_regularizer
+            M[i+b1,i+b1]=self.L2_regularizer
         data = trainset.fields()
         train_inputs = numpy.array(data['input'])
-        Y[0]=1
-        Y[1:,:] = numpy.array(data['target'])
+        if self.use_bias:
+            Y[0]=1
+        Y[b1:,:] = numpy.array(data['target'])
         train_inputs_square,sumG,G=self.equations.compute_system_matrix(train_inputs,self.gamma)
-        M[1:,1:] += G 
-        M[0,1:] = sumG
-        M[1:,0] = 1
-        M[0,0] = M.shape[0]
+        M[b1:,b1:] += G
+        if self.use_bias:
+            M[0,1:] = sumG
+            M[1:,0] = 1
+            M[0,0] = M.shape[0]
         self.M=M
         self.Y=Y
         theta=numpy.linalg.solve(M,Y)
@@ -117,10 +122,11 @@
     inputs = T.matrix() # minibatchsize x n_inputs
     targets = T.matrix() # minibatchsize x n_outputs
     theta = T.matrix() # (n_examples+1) x n_outputs
+    b1 = T.shape(train_inputs_square)[0]<T.shape(theta)[0]
     gamma = T.scalar()
     inv_gamma2 = 1./(gamma*gamma)
-    b = theta[0]
-    alpha = theta[1:,:]
+    b = b1*theta[0]
+    alpha = theta[b1:,:]
     inputs_square = T.sum(inputs*inputs,axis=1)
     Kx = T.exp(-(row_vector(train_inputs_square)-2*T.dot(inputs,train_inputs.T)+col_vector(inputs_square))*inv_gamma2)
     outputs = T.dot(Kx,alpha) + b # minibatchsize x n_outputs