Added p and TV regularisation.

pyramid/regularisator.py

@@ -12,7 +12,8 @@ import numpy as np
 
 from scipy.sparse import coo_matrix, csr_matrix
 import jutil.norms as jnorm
-
+import jutil.diff as jdiff
+import jutil.operator as joperator
 from pyramid.converter import IndexConverter
 
 import logging
@@ -103,9 +104,13 @@ class ZeroOrderRegularisator(Regularisator):
 
     LOG = logging.getLogger(__name__+'.ZeroOrderRegularisator')
 
-    def __init__(self, lam):
+    def __init__(self, _, lam, p=2):
         self.LOG.debug('Calling __init__')
-        norm = jnorm.L2Square()
+        self.p = p
+        if p == 2:
+            norm = jnorm.L2Square()
+        else:
+            norm = jnorm.LPPow(p, 1e-12)
         super(ZeroOrderRegularisator, self).__init__(norm, lam)
         self.LOG.debug('Created '+str(self))
 
@@ -113,11 +118,14 @@ class ZeroOrderRegularisator(Regularisator):
 class FirstOrderRegularisator(Regularisator):
     # TODO: Docstring!
 
-    def __init__(self, mask, lam, x_a=None):
-        import jutil
-        D0 = jutil.diff.get_diff_operator(mask, 0, 3)
-        D1 = jutil.diff.get_diff_operator(mask, 1, 3)
-        D = jutil.operator.VStack([D0, D1])
-        norm = jutil.norms.WeightedL2Square(D)
+    def __init__(self, mask, lam, p=2):
+        self.p = p
+        D0 = jdiff.get_diff_operator(mask, 0, 3)
+        D1 = jdiff.get_diff_operator(mask, 1, 3)
+        D = joperator.VStack([D0, D1])
+        if p == 2:
+            norm = jnorm.WeightedL2Square(D)
+        else:
+            norm = jnorm.WeightedTV(jnorm.LPPow(p, 1e-12), D, [D0.shape[0], D.shape[0]])
         super(FirstOrderRegularisator, self).__init__(norm, lam)
         self.LOG.debug('Created '+str(self))