give the wrapper a better name

1 files changed, 0 insertions, 123 deletions

diff --git a/src/pyx_gensvm.pyx b/src/pyx_gensvm.pyx
deleted file mode 100644
index 394d4ca..0000000
--- a/src/pyx_gensvm.pyx
+++ /dev/null
@@ -1,123 +0,0 @@
-"""
-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)