1 files changed, 123 insertions, 0 deletions
diff --git a/gensvm/pyx_gensvm.pyx b/gensvm/pyx_gensvm.pyx
new file mode 100644
index 0000000..394d4ca
--- /dev/null
+++ b/gensvm/pyx_gensvm.pyx
@@ -0,0 +1,123 @@
+"""
+Wrapper for GenSVM
+
+Not implemented yet:
+    - vector of instance weights
+    - class weights
+    - seed model
+    - max_iter = -1 for unlimited
+
+"""
+
+from __future__ import print_function
+
+import numpy as np
+cimport numpy as np
+
+cimport pyx_gensvm
+
+np.import_array()
+
+GENSVM_KERNEL_TYPES = ["linear", "poly", "rbf", "sigmoid"]
+
+def train_wrap(
+        np.ndarray[np.float64_t, ndim=2, mode='c'] X,
+        np.ndarray[np.int_t, ndim=1, mode='c'] y,
+        double p=1.0,
+        double lmd=pow(2, -8),
+        double kappa=0.0,
+        double epsilon=1e-6,
+        int weight_idx=1,
+        str kernel='linear',
+        double gamma=1.0,
+        double coef=0.0,
+        double degree=2.0,
+        double kernel_eigen_cutoff=1e-8,
+        int max_iter=100000000,
+        int random_seed=-1):
+    """
+    """
+
+    # Initialize model and data
+    cdef GenModel *model = gensvm_init_model()
+    cdef GenData *data = gensvm_init_data()
+    cdef long n_obs
+    cdef long n_var
+    cdef long n_class
+
+    # get the kernel index
+    kernel_index = GENSVM_KERNEL_TYPES.index(kernel)
+
+    # get the number of classes
+    classes = np.unique(y)
+    n_obs = X.shape[0]
+    n_var = X.shape[1]
+    n_class = classes.shape[0]
+
+    # Set the data
+    set_data(data, X.data, y.data, X.shape, n_class)
+
+    # Set the model
+    set_model(model, p, lmd, kappa, epsilon, weight_idx, kernel_index, degree, 
+            gamma, coef, kernel_eigen_cutoff, max_iter, random_seed)
+
+    # Check the parameters
+    error_msg = check_model(model)
+    if error_msg:
+        gensvm_free_model(model)
+        free_data(data)
+        error_repl = error_msg.decode('utf-8')
+        raise ValueError(error_repl)
+
+    # Do the actual training
+    with nogil:
+        gensvm_train(model, data, NULL)
+
+    # copy the results
+    cdef np.ndarray[np.float64_t, ndim=2, mode='c'] V
+    V = np.empty((n_var+1, n_class-1))
+    copy_V(V.data, model)
+
+    # get other results from model
+    iter_count = get_iter_count(model)
+    training_error = get_training_error(model)
+    fit_status = get_status(model)
+    n_SV = gensvm_num_sv(model)
+
+    # free model and data
+    gensvm_free_model(model);
+    free_data(data);
+
+    return (V, n_SV, iter_count, training_error, fit_status)
+
+def predict_wrap(
+        np.ndarray[np.float64_t, ndim=2, mode='c'] X,
+        np.ndarray[np.float64_t, ndim=2, mode='c'] V
+        ):
+    """
+    """
+
+    cdef long n_test_obs
+    cdef long n_var
+    cdef long n_class
+
+    n_test_obs = X.shape[0]
+    n_var = X.shape[1]
+    n_class = V.shape[1] + 1
+
+    # output vector
+    cdef np.ndarray[np.int_t, ndim=1, mode='c'] predictions
+    predictions = np.empty((n_test_obs, ), dtype=np.int)
+
+    # do the prediction
+    with nogil:
+        gensvm_predict(X.data, V.data, n_test_obs, n_var, n_class, 
+                predictions.data)
+
+    return predictions
+
+def set_verbosity_wrap(int verbosity):
+    """
+    Control verbosity of gensvm library
+    """
+    set_verbosity(verbosity)