Init commit with plsa

plsa/plsa/_plsa.pyx

@@ -0,0 +1,72 @@
+import numpy as np
+cimport numpy as np
+
+cdef extern void _train(unsigned int n_ele,
+                        unsigned int n_z,
+                        unsigned int n_w,
+                        unsigned int n_d,
+                        unsigned int *tdnz,
+                        double *p_z,
+                        double *p_w_z,
+                        double *p_d_z,
+                        double *p_z_old,
+                        double *p_w_z_old,
+                        double *p_d_z_old,
+                        unsigned int maxiter,
+                        double eps,
+                        double beta,
+                        unsigned int min_iteration,
+                        unsigned int folding_in,
+                        unsigned int debug,
+                        double *logL_c)
+
+def nonzero(td):
+    """
+    Convert a sparse matrix td to a Nx3 matrix where N is the number of non-zero
+    elements. The 1st column is the word count, the 2nd is the word index
+    and the 3rd column is the document index.
+    """
+    rows, cols = td.nonzero()
+    vals = td[rows,cols]
+    if "scipy.sparse" in str(vals.__class__): vals = vals.toarray()
+    return np.asfortranarray(np.vstack((vals, rows, cols)).T)
+
+def train(td,
+          np.ndarray[np.float64_t, ndim=1, mode='c']p_z,
+          np.ndarray[np.float64_t, ndim=2, mode='c']p_w_z,
+          np.ndarray[np.float64_t, ndim=2, mode='c']p_d_z,
+          np.ndarray[np.float64_t, ndim=1, mode='c']p_z_old,
+          np.ndarray[np.float64_t, ndim=2, mode='c']p_w_z_old,
+          np.ndarray[np.float64_t, ndim=2, mode='c']p_d_z_old,
+          maxiter,
+          eps,
+          beta,
+          min_iteration,
+          folding_in,
+          debug,
+          np.ndarray[np.float64_t, ndim=1, mode='c']logL_c):
+
+    cdef np.ndarray[np.uint32_t, ndim=2, mode='fortran'] tdnz
+
+    tdnz = nonzero(td).astype(np.uint32)
+
+    _train(<unsigned int>tdnz.shape[0],
+           <unsigned int>p_z.shape[0],
+           <unsigned int>p_w_z.shape[0],
+           <unsigned int>p_d_z.shape[0],
+           <unsigned int *>tdnz.data,
+           <double *>p_z.data,
+           <double *>p_w_z.data,
+           <double *>p_d_z.data,
+           <double *>p_z_old.data,
+           <double *>p_w_z_old.data,
+           <double *>p_d_z_old.data,
+           <unsigned int>maxiter,
+           <double> eps,
+           <double> beta,
+           <unsigned int> min_iteration,
+           <unsigned int>folding_in,
+           <unsigned int>debug,
+           <double *>logL_c.data)
+
+