{{py:

"""
Efficient (dense) parameter vector implementation for linear models.

Template file for easily generate fused types consistent code using Tempita
(https://github.com/cython/cython/blob/master/Cython/Tempita/_tempita.py).

Generated file: weight_vector.pxd

Each class is duplicated for all dtypes (float and double). The keywords
between double braces are substituted in setup.py.
"""

# name_suffix, c_type
dtypes = [('64', 'double'),
          ('32', 'float')]

}}

# WARNING: Do not edit this .pyx file directly, it is generated from its .pyx.tp
cimport numpy as np

{{for name_suffix, c_type in dtypes}}

cdef class WeightVector{{name_suffix}}(object):
    cdef readonly {{c_type}}[::1] w
    cdef readonly {{c_type}}[::1] aw
    cdef {{c_type}} *w_data_ptr
    cdef {{c_type}} *aw_data_ptr
    cdef {{c_type}} wscale
    cdef {{c_type}} average_a
    cdef {{c_type}} average_b
    cdef int n_features
    cdef {{c_type}} sq_norm

    cdef void add(self, {{c_type}} *x_data_ptr, int *x_ind_ptr,
                  int xnnz, {{c_type}} c) nogil
    cdef void add_average(self, {{c_type}} *x_data_ptr, int *x_ind_ptr,
                          int xnnz, {{c_type}} c, {{c_type}} num_iter) nogil
    cdef {{c_type}} dot(self, {{c_type}} *x_data_ptr, int *x_ind_ptr,
                    int xnnz) nogil
    cdef void scale(self, {{c_type}} c) nogil
    cdef void reset_wscale(self) nogil
    cdef {{c_type}} norm(self) nogil

{{endfor}}