Add code for prediction with kernels

3 files changed, 74 insertions, 3 deletions

diff --git a/gensvm/core.py b/gensvm/core.py
index a2c4cd0..ce416d0 100644
--- a/gensvm/core.py
+++ b/gensvm/core.py
@@ -343,20 +343,50 @@ class GenSVM(BaseEstimator, ClassifierMixin):
         )
         return self
 
-    def predict(self, X):
+    def predict(self, X, trainX=None):
         """Predict the class labels on the given data
 
         Parameters
         ----------
-        X : array, shape = [n_samples, n_features]
+        X : array, shape = [n_test_samples, n_features]
+            Data for which to predict the labels
+
+        trainX : array, shape = [n_train_samples, n_features]
+            Only for nonlinear prediction with kernels: the training data used 
+            to train the model.
 
         Returns
         -------
         y_pred : array, shape = (n_samples, )
 
         """
+
+        if (not self.kernel == "linear") and trainX is None:
+            raise ValueError(
+                "Training data must be provided with nonlinear prediction"
+            )
+        if not trainX is None and not X.shape[1] == trainX.shape[1]:
+            raise ValueError(
+                "Test and training data should have the same number of features"
+            )
+
         V = self.combined_coef_
-        predictions = wrapper.predict_wrap(X, V)
+        if self.kernel == "linear":
+            predictions = wrapper.predict_wrap(X, V)
+        else:
+            n_class = len(self.encoder.classes_)
+            kernel_idx = wrapper.GENSVM_KERNEL_TYPES.index(self.kernel)
+            predictions = wrapper.predict_kernels_wrap(
+                X,
+                trainX,
+                V,
+                n_class,
+                kernel_idx,
+                self.gamma,
+                self.coef,
+                self.degree,
+                self.kernel_eigen_cutoff,
+            )
 
         # Transform the classes back to the original form
         predictions -= 1
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,