Add code for prediction with kernels

2 files changed, 41 insertions, 0 deletions

diff --git a/gensvm/cython_wrapper/wrapper.pxd b/gensvm/cython_wrapper/wrapper.pxd
index 441c15b..6a896aa 100644
--- a/gensvm/cython_wrapper/wrapper.pxd
+++ b/gensvm/cython_wrapper/wrapper.pxd
@@ -129,6 +129,8 @@ cdef extern from "gensvm_helper.c":
     void free_data(GenData *)
     void set_verbosity(int)
     void gensvm_predict(char *, char *, long, long, long, char *) nogil
+    void gensvm_predict_kernels(char *, char *, char *, long, long, long, 
+            long, long, long, int, double,  double, double, double, char *) nogil
     void gensvm_train_q_helper(GenQueue *, char *, int) nogil
     void set_queue(GenQueue *, long, GenTask **)
     double get_task_duration(GenTask *)
diff --git a/gensvm/cython_wrapper/wrapper.pyx b/gensvm/cython_wrapper/wrapper.pyx
index 6d5bbae..e138620 100644
--- a/gensvm/cython_wrapper/wrapper.pyx
+++ b/gensvm/cython_wrapper/wrapper.pyx
@@ -133,6 +133,45 @@ def predict_wrap(
 
     return predictions
 
+
+def predict_kernels_wrap(
+        np.ndarray[np.float64_t, ndim=2, mode='c'] Xtest,
+        np.ndarray[np.float64_t, ndim=2, mode='c'] Xtrain,
+        np.ndarray[np.float64_t, ndim=2, mode='c'] V,
+        long n_class,
+        int kernel_idx,
+        double gamma,
+        double coef,
+        double degree,
+        double kernel_eigen_cutoff
+        ):
+    """
+    Compute predictions for nonlinear GenSVM. Calls the C helper function 
+    "gensvm_predict_kernels", which in turn calls the appropriate library 
+    functions.
+    """
+
+    cdef long n_obs_test
+    cdef long n_obs_train
+    cdef long n_var
+    cdef long V_rows = V.shape[0]
+    cdef long V_cols = V.shape[1]
+
+    n_obs_test = Xtest.shape[0]
+    n_obs_train = Xtrain.shape[0]
+    n_var = Xtrain.shape[1]
+
+    cdef np.ndarray[np.int_t, ndim=1, mode='c'] predictions
+    predictions = np.empty((n_obs_test, ), dtype=np.int)
+
+    with nogil:
+        gensvm_predict_kernels(Xtest.data, Xtrain.data, V.data, V_rows, 
+                V_cols, n_obs_train, n_obs_test, n_var, n_class, kernel_idx, 
+                gamma, coef, degree, kernel_eigen_cutoff, predictions.data)
+
+    return predictions
+
+
 def grid_wrap(
         np.ndarray[np.float64_t, ndim=2, mode='c'] X,
         np.ndarray[np.int_t, ndim=1, mode='c'] y,