Added decorators suggested by @matteofrigo

daducci · Jun 1, 2018 · 1b69c3f · 1b69c3f
1 parent 7070cff
commit 1b69c3f
Showing 1 changed file with 7 additions and 4 deletions.
diff --git a/commit/proximals.pyx b/commit/proximals.pyx
@@ -1,3 +1,5 @@
+#!python
+#cython: boundscheck=False, wraparound=False
 """
 Author: Matteo Frigo - lts5 @ EPFL and Dep. of CS @ Univ. of Verona
 
@@ -10,9 +12,6 @@ cimport numpy as np
 from math import sqrt
 import sys
 
-@cython.boundscheck(False)
-@cython.wraparound(False)
-@cython.profile(False)
 
 cpdef non_negativity(np.ndarray[np.float64_t] x, int compartment_start, int compartment_size):
     """
@@ -27,6 +26,7 @@ cpdef non_negativity(np.ndarray[np.float64_t] x, int compartment_start, int comp
             v[i] = 0.0
     return v
 
+
 cpdef soft_thresholding(np.ndarray[np.float64_t] x, double lam, int compartment_start, int compartment_size) :
     """
     Proximal of L1 norm
@@ -43,6 +43,7 @@ cpdef soft_thresholding(np.ndarray[np.float64_t] x, double lam, int compartment_
             v[i] -= lam
     return v
 
+
 cpdef projection_onto_l2_ball(np.ndarray[np.float64_t] x, double lam, int compartment_start, int compartment_size) :
     """
     Proximal of L2 norm
@@ -59,6 +60,7 @@ cpdef projection_onto_l2_ball(np.ndarray[np.float64_t] x, double lam, int compar
             v[i] = v[i]/xn*lam
     return v
 
+
 cpdef omega_group_sparsity(np.ndarray[np.float64_t] v, np.ndarray[object] subtree, np.ndarray[np.float64_t] weight, double lam, double n) :
     """
     References:
@@ -80,14 +82,15 @@ cpdef omega_group_sparsity(np.ndarray[np.float64_t] v, np.ndarray[object] subtre
                 tmp += weight[k] * max( v[idx] )
     return lam*tmp
 
+
 cpdef prox_group_sparsity( np.ndarray[np.float64_t] x, np.ndarray[object] subtree, np.ndarray[np.float64_t] weight, double lam, double n ) :
     """
     References:
         [1] Jenatton et al. - `Proximal Methods for Hierarchical Sparse Coding`
     """
     cdef:
         np.ndarray[np.float64_t] v
-        int nG = weight.size, N, rho
+        int nG = weight.size
         size_t k, i
         double r, xn