# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#         Fabian Pedregosa <fabian.pedregosa@inria.fr>
#         Olivier Grisel <olivier.grisel@ensta.org>
#         Alexis Mignon <alexis.mignon@gmail.com>
#         Manoj Kumar <manojkumarsivaraj334@gmail.com>
#
# License: BSD 3 clause
#
# cython: boundscheck=False, wraparound=False, cdivision=True

from libc.math cimport fabs
cimport numpy as np
import numpy as np
import numpy.linalg as linalg

cimport cython
from cpython cimport bool
from cython cimport floating
import warnings
from ..exceptions import ConvergenceWarning

from ..utils._cython_blas cimport (_axpy, _dot, _asum, _ger, _gemv, _nrm2, 
                                   _copy, _scal)
from ..utils._cython_blas cimport RowMajor, ColMajor, Trans, NoTrans


from ..utils._random cimport our_rand_r

ctypedef np.float64_t DOUBLE
ctypedef np.uint32_t UINT32_t

np.import_array()

# The following two functions are shamelessly copied from the tree code.

cdef enum:
    # Max value for our rand_r replacement (near the bottom).
    # We don't use RAND_MAX because it's different across platforms and
    # particularly tiny on Windows/MSVC.
    RAND_R_MAX = 0x7FFFFFFF


cdef inline UINT32_t rand_int(UINT32_t end, UINT32_t* random_state) nogil:
    """Generate a random integer in [0; end)."""
    return our_rand_r(random_state) % end


cdef inline floating fmax(floating x, floating y) nogil:
    if x > y:
        return x
    return y


cdef inline floating fsign(floating f) nogil:
    if f == 0:
        return 0
    elif f > 0:
        return 1.0
    else:
        return -1.0


cdef floating abs_max(int n, floating* a) nogil:
    """np.max(np.abs(a))"""
    cdef int i
    cdef floating m = fabs(a[0])
    cdef floating d
    for i in range(1, n):
        d = fabs(a[i])
        if d > m:
            m = d
    return m


cdef floating max(int n, floating* a) nogil:
    """np.max(a)"""
    cdef int i
    cdef floating m = a[0]
    cdef floating d
    for i in range(1, n):
        d = a[i]
        if d > m:
            m = d
    return m


cdef floating diff_abs_max(int n, floating* a, floating* b) nogil:
    """np.max(np.abs(a - b))"""
    cdef int i
    cdef floating m = fabs(a[0] - b[0])
    cdef floating d
    for i in range(1, n):
        d = fabs(a[i] - b[i])
        if d > m:
            m = d
    return m


def enet_coordinate_descent(floating[::1] w,
                            floating alpha, floating beta,
                            floating[::1, :] X,
                            floating[::1] y,
                            int max_iter, floating tol,
                            object rng, bint random=0, bint positive=0):
    """Cython version of the coordinate descent algorithm
        for Elastic-Net regression

        We minimize

        (1/2) * norm(y - X w, 2)^2 + alpha norm(w, 1) + (beta/2) norm(w, 2)^2

    """

    if floating is float:
        dtype = np.float32
    else:
        dtype = np.float64

    # get the data information into easy vars
    cdef unsigned int n_samples = X.shape[0]
    cdef unsigned int n_features = X.shape[1]

    # compute norms of the columns of X
    cdef floating[::1] norm_cols_X = np.square(X).sum(axis=0)

    # initial value of the residuals
    cdef floating[::1] R = np.empty(n_samples, dtype=dtype)
    cdef floating[::1] XtA = np.empty(n_features, dtype=dtype)

    cdef floating tmp
    cdef floating w_ii
    cdef floating d_w_max
    cdef floating w_max
    cdef floating d_w_ii
    cdef floating gap = tol + 1.0
    cdef floating d_w_tol = tol
    cdef floating dual_norm_XtA
    cdef floating R_norm2
    cdef floating w_norm2
    cdef floating l1_norm
    cdef floating const
    cdef floating A_norm2
    cdef unsigned int ii
    cdef unsigned int i
    cdef unsigned int n_iter = 0
    cdef unsigned int f_iter
    cdef UINT32_t rand_r_state_seed = rng.randint(0, RAND_R_MAX)
    cdef UINT32_t* rand_r_state = &rand_r_state_seed

    if alpha == 0 and beta == 0:
        warnings.warn("Coordinate descent with no regularization may lead to "
                      "unexpected results and is discouraged.")

    with nogil:
        # R = y - np.dot(X, w)
        _copy(n_samples, &y[0], 1, &R[0], 1)
        _gemv(ColMajor, NoTrans, n_samples, n_features, -1.0, &X[0, 0], 
              n_samples, &w[0], 1, 1.0, &R[0], 1)

        # tol *= np.dot(y, y)
        tol *= _dot(n_samples, &y[0], 1, &y[0], 1)

        for n_iter in range(max_iter):
            w_max = 0.0
            d_w_max = 0.0
            for f_iter in range(n_features):  # Loop over coordinates
                if random:
                    ii = rand_int(n_features, rand_r_state)
                else:
                    ii = f_iter

                if norm_cols_X[ii] == 0.0:
                    continue

                w_ii = w[ii]  # Store previous value

                if w_ii != 0.0:
                    # R += w_ii * X[:,ii]
                    _axpy(n_samples, w_ii, &X[0, ii], 1, &R[0], 1)

                # tmp = (X[:,ii]*R).sum()
                tmp = _dot(n_samples, &X[0, ii], 1, &R[0], 1)

                if positive and tmp < 0:
                    w[ii] = 0.0
                else:
                    w[ii] = (fsign(tmp) * fmax(fabs(tmp) - alpha, 0)
                             / (norm_cols_X[ii] + beta))

                if w[ii] != 0.0:
                    # R -=  w[ii] * X[:,ii] # Update residual
                    _axpy(n_samples, -w[ii], &X[0, ii], 1, &R[0], 1)

                # update the maximum absolute coefficient update
                d_w_ii = fabs(w[ii] - w_ii)
                d_w_max = fmax(d_w_max, d_w_ii)

                w_max = fmax(w_max, fabs(w[ii]))

            if (w_max == 0.0 or
                d_w_max / w_max < d_w_tol or
                n_iter == max_iter - 1):
                # the biggest coordinate update of this iteration was smaller
                # than the tolerance: check the duality gap as ultimate
                # stopping criterion

                # XtA = np.dot(X.T, R) - beta * w
                _copy(n_features, &w[0], 1, &XtA[0], 1)
                _gemv(ColMajor, Trans,
                      n_samples, n_features, 1.0, &X[0, 0], n_samples,
                      &R[0], 1,
                      -beta, &XtA[0], 1)

                if positive:
                    dual_norm_XtA = max(n_features, &XtA[0])
                else:
                    dual_norm_XtA = abs_max(n_features, &XtA[0])

                # R_norm2 = np.dot(R, R)
                R_norm2 = _dot(n_samples, &R[0], 1, &R[0], 1)

                # w_norm2 = np.dot(w, w)
                w_norm2 = _dot(n_features, &w[0], 1, &w[0], 1)

                if (dual_norm_XtA > alpha):
                    const = alpha / dual_norm_XtA
                    A_norm2 = R_norm2 * (const ** 2)
                    gap = 0.5 * (R_norm2 + A_norm2)
                else:
                    const = 1.0
                    gap = R_norm2

                l1_norm = _asum(n_features, &w[0], 1)

                # np.dot(R.T, y)
                gap += (alpha * l1_norm
                        - const * _dot(n_samples, &R[0], 1, &y[0], 1)
                        + 0.5 * beta * (1 + const ** 2) * (w_norm2))

                if gap < tol:
                    # return if we reached desired tolerance
                    break

        else:
            # for/else, runs if for doesn't end with a `break`
            with gil:
                warnings.warn("Objective did not converge. You might want to "
                              "increase the number of iterations. Duality "
                              "gap: {}, tolerance: {}".format(gap, tol),
                              ConvergenceWarning)

    return w, gap, tol, n_iter + 1


def sparse_enet_coordinate_descent(floating [::1] w,
                            floating alpha, floating beta,
                            np.ndarray[floating, ndim=1, mode='c'] X_data,
                            np.ndarray[int, ndim=1, mode='c'] X_indices,
                            np.ndarray[int, ndim=1, mode='c'] X_indptr,
                            np.ndarray[floating, ndim=1] y,
                            floating[:] X_mean, int max_iter,
                            floating tol, object rng, bint random=0,
                            bint positive=0):
    """Cython version of the coordinate descent algorithm for Elastic-Net

    We minimize:

        (1/2) * norm(y - X w, 2)^2 + alpha norm(w, 1) + (beta/2) * norm(w, 2)^2

    """

    # get the data information into easy vars
    cdef unsigned int n_samples = y.shape[0]
    cdef unsigned int n_features = w.shape[0]

    # compute norms of the columns of X
    cdef unsigned int ii
    cdef floating[:] norm_cols_X

    cdef unsigned int startptr = X_indptr[0]
    cdef unsigned int endptr

    # initial value of the residuals
    cdef floating[:] R = y.copy()

    cdef floating[:] X_T_R
    cdef floating[:] XtA

    if floating is float:
        dtype = np.float32
    else:
        dtype = np.float64

    norm_cols_X = np.zeros(n_features, dtype=dtype)
    X_T_R = np.zeros(n_features, dtype=dtype)
    XtA = np.zeros(n_features, dtype=dtype)

    cdef floating tmp
    cdef floating w_ii
    cdef floating d_w_max
    cdef floating w_max
    cdef floating d_w_ii
    cdef floating X_mean_ii
    cdef floating R_sum = 0.0
    cdef floating R_norm2
    cdef floating w_norm2
    cdef floating A_norm2
    cdef floating l1_norm
    cdef floating normalize_sum
    cdef floating gap = tol + 1.0
    cdef floating d_w_tol = tol
    cdef floating dual_norm_XtA
    cdef unsigned int jj
    cdef unsigned int n_iter = 0
    cdef unsigned int f_iter
    cdef UINT32_t rand_r_state_seed = rng.randint(0, RAND_R_MAX)
    cdef UINT32_t* rand_r_state = &rand_r_state_seed
    cdef bint center = False

    with nogil:
        # center = (X_mean != 0).any()
        for ii in range(n_features):
            if X_mean[ii]:
                center = True
                break

        for ii in range(n_features):
            X_mean_ii = X_mean[ii]
            endptr = X_indptr[ii + 1]
            normalize_sum = 0.0
            w_ii = w[ii]

            for jj in range(startptr, endptr):
                normalize_sum += (X_data[jj] - X_mean_ii) ** 2
                R[X_indices[jj]] -= X_data[jj] * w_ii
            norm_cols_X[ii] = normalize_sum + \
                (n_samples - endptr + startptr) * X_mean_ii ** 2

            if center:
                for jj in range(n_samples):
                    R[jj] += X_mean_ii * w_ii
            startptr = endptr

        # tol *= np.dot(y, y)
        tol *= _dot(n_samples, &y[0], 1, &y[0], 1)

        for n_iter in range(max_iter):

            w_max = 0.0
            d_w_max = 0.0

            for f_iter in range(n_features):  # Loop over coordinates
                if random:
                    ii = rand_int(n_features, rand_r_state)
                else:
                    ii = f_iter

                if norm_cols_X[ii] == 0.0:
                    continue

                startptr = X_indptr[ii]
                endptr = X_indptr[ii + 1]
                w_ii = w[ii]  # Store previous value
                X_mean_ii = X_mean[ii]

                if w_ii != 0.0:
                    # R += w_ii * X[:,ii]
                    for jj in range(startptr, endptr):
                        R[X_indices[jj]] += X_data[jj] * w_ii
                    if center:
                        for jj in range(n_samples):
                            R[jj] -= X_mean_ii * w_ii

                # tmp = (X[:,ii] * R).sum()
                tmp = 0.0
                for jj in range(startptr, endptr):
                    tmp += R[X_indices[jj]] * X_data[jj]

                if center:
                    R_sum = 0.0
                    for jj in range(n_samples):
                        R_sum += R[jj]
                    tmp -= R_sum * X_mean_ii

                if positive and tmp < 0.0:
                    w[ii] = 0.0
                else:
                    w[ii] = fsign(tmp) * fmax(fabs(tmp) - alpha, 0) \
                            / (norm_cols_X[ii] + beta)

                if w[ii] != 0.0:
                    # R -=  w[ii] * X[:,ii] # Update residual
                    for jj in range(startptr, endptr):
                        R[X_indices[jj]] -= X_data[jj] * w[ii]

                    if center:
                        for jj in range(n_samples):
                            R[jj] += X_mean_ii * w[ii]

                # update the maximum absolute coefficient update
                d_w_ii = fabs(w[ii] - w_ii)
                if d_w_ii > d_w_max:
                    d_w_max = d_w_ii

                if fabs(w[ii]) > w_max:
                    w_max = fabs(w[ii])

            if w_max == 0.0 or d_w_max / w_max < d_w_tol or n_iter == max_iter - 1:
                # the biggest coordinate update of this iteration was smaller than
                # the tolerance: check the duality gap as ultimate stopping
                # criterion

                # sparse X.T / dense R dot product
                if center:
                    R_sum = 0.0
                    for jj in range(n_samples):
                        R_sum += R[jj]

                for ii in range(n_features):
                    X_T_R[ii] = 0.0
                    for jj in range(X_indptr[ii], X_indptr[ii + 1]):
                        X_T_R[ii] += X_data[jj] * R[X_indices[jj]]

                    if center:
                        X_T_R[ii] -= X_mean[ii] * R_sum
                    XtA[ii] = X_T_R[ii] - beta * w[ii]

                if positive:
                    dual_norm_XtA = max(n_features, &XtA[0])
                else:
                    dual_norm_XtA = abs_max(n_features, &XtA[0])

                # R_norm2 = np.dot(R, R)
                R_norm2 = _dot(n_samples, &R[0], 1, &R[0], 1)

                # w_norm2 = np.dot(w, w)
                w_norm2 = _dot(n_features, &w[0], 1, &w[0], 1)
                if (dual_norm_XtA > alpha):
                    const = alpha / dual_norm_XtA
                    A_norm2 = R_norm2 * const**2
                    gap = 0.5 * (R_norm2 + A_norm2)
                else:
                    const = 1.0
                    gap = R_norm2

                l1_norm = _asum(n_features, &w[0], 1)

                gap += (alpha * l1_norm - const * _dot(
                            n_samples,
                            &R[0], 1,
                            &y[0], 1
                            )
                        + 0.5 * beta * (1 + const ** 2) * w_norm2)

                if gap < tol:
                    # return if we reached desired tolerance
                    break

        else:
            # for/else, runs if for doesn't end with a `break`
            with gil:
                warnings.warn("Objective did not converge. You might want to "
                              "increase the number of iterations. Duality "
                              "gap: {}, tolerance: {}".format(gap, tol),
                              ConvergenceWarning)

    return w, gap, tol, n_iter + 1


def enet_coordinate_descent_gram(floating[::1] w,
                                 floating alpha, floating beta,
                                 np.ndarray[floating, ndim=2, mode='c'] Q,
                                 np.ndarray[floating, ndim=1, mode='c'] q,
                                 np.ndarray[floating, ndim=1] y,
                                 int max_iter, floating tol, object rng,
                                 bint random=0, bint positive=0):
    """Cython version of the coordinate descent algorithm
        for Elastic-Net regression

        We minimize

        (1/2) * w^T Q w - q^T w + alpha norm(w, 1) + (beta/2) * norm(w, 2)^2

        which amount to the Elastic-Net problem when:
        Q = X^T X (Gram matrix)
        q = X^T y
    """

    if floating is float:
        dtype = np.float32
    else:
        dtype = np.float64

    # get the data information into easy vars
    cdef unsigned int n_samples = y.shape[0]
    cdef unsigned int n_features = Q.shape[0]

    # initial value "Q w" which will be kept of up to date in the iterations
    cdef floating[:] H = np.dot(Q, w)

    cdef floating[:] XtA = np.zeros(n_features, dtype=dtype)
    cdef floating tmp
    cdef floating w_ii
    cdef floating d_w_max
    cdef floating w_max
    cdef floating d_w_ii
    cdef floating q_dot_w
    cdef floating w_norm2
    cdef floating gap = tol + 1.0
    cdef floating d_w_tol = tol
    cdef floating dual_norm_XtA
    cdef unsigned int ii
    cdef unsigned int n_iter = 0
    cdef unsigned int f_iter
    cdef UINT32_t rand_r_state_seed = rng.randint(0, RAND_R_MAX)
    cdef UINT32_t* rand_r_state = &rand_r_state_seed

    cdef floating y_norm2 = np.dot(y, y)
    cdef floating* w_ptr = <floating*>&w[0]
    cdef floating* Q_ptr = &Q[0, 0]
    cdef floating* q_ptr = <floating*>q.data
    cdef floating* H_ptr = &H[0]
    cdef floating* XtA_ptr = &XtA[0]
    tol = tol * y_norm2

    if alpha == 0:
        warnings.warn("Coordinate descent with alpha=0 may lead to unexpected"
            " results and is discouraged.")

    with nogil:
        for n_iter in range(max_iter):
            w_max = 0.0
            d_w_max = 0.0
            for f_iter in range(n_features):  # Loop over coordinates
                if random:
                    ii = rand_int(n_features, rand_r_state)
                else:
                    ii = f_iter

                if Q[ii, ii] == 0.0:
                    continue

                w_ii = w[ii]  # Store previous value

                if w_ii != 0.0:
                    # H -= w_ii * Q[ii]
                    _axpy(n_features, -w_ii, Q_ptr + ii * n_features, 1,
                          H_ptr, 1)

                tmp = q[ii] - H[ii]

                if positive and tmp < 0:
                    w[ii] = 0.0
                else:
                    w[ii] = fsign(tmp) * fmax(fabs(tmp) - alpha, 0) \
                        / (Q[ii, ii] + beta)

                if w[ii] != 0.0:
                    # H +=  w[ii] * Q[ii] # Update H = X.T X w
                    _axpy(n_features, w[ii], Q_ptr + ii * n_features, 1,
                          H_ptr, 1)

                # update the maximum absolute coefficient update
                d_w_ii = fabs(w[ii] - w_ii)
                if d_w_ii > d_w_max:
                    d_w_max = d_w_ii

                if fabs(w[ii]) > w_max:
                    w_max = fabs(w[ii])

            if w_max == 0.0 or d_w_max / w_max < d_w_tol or n_iter == max_iter - 1:
                # the biggest coordinate update of this iteration was smaller than
                # the tolerance: check the duality gap as ultimate stopping
                # criterion

                # q_dot_w = np.dot(w, q)
                q_dot_w = _dot(n_features, w_ptr, 1, q_ptr, 1)

                for ii in range(n_features):
                    XtA[ii] = q[ii] - H[ii] - beta * w[ii]
                if positive:
                    dual_norm_XtA = max(n_features, XtA_ptr)
                else:
                    dual_norm_XtA = abs_max(n_features, XtA_ptr)

                # temp = np.sum(w * H)
                tmp = 0.0
                for ii in range(n_features):
                    tmp += w[ii] * H[ii]
                R_norm2 = y_norm2 + tmp - 2.0 * q_dot_w

                # w_norm2 = np.dot(w, w)
                w_norm2 = _dot(n_features, &w[0], 1, &w[0], 1)

                if (dual_norm_XtA > alpha):
                    const = alpha / dual_norm_XtA
                    A_norm2 = R_norm2 * (const ** 2)
                    gap = 0.5 * (R_norm2 + A_norm2)
                else:
                    const = 1.0
                    gap = R_norm2

                # The call to asum is equivalent to the L1 norm of w
                gap += (alpha * _asum(n_features, &w[0], 1) -
                        const * y_norm2 +  const * q_dot_w +
                        0.5 * beta * (1 + const ** 2) * w_norm2)

                if gap < tol:
                    # return if we reached desired tolerance
                    break

        else:
            # for/else, runs if for doesn't end with a `break`
            with gil:
                warnings.warn("Objective did not converge. You might want to "
                              "increase the number of iterations. Duality "
                              "gap: {}, tolerance: {}".format(gap, tol),
                              ConvergenceWarning)

    return np.asarray(w), gap, tol, n_iter + 1


def enet_coordinate_descent_multi_task(floating[::1, :] W, floating l1_reg,
                                       floating l2_reg,
                                       np.ndarray[floating, ndim=2, mode='fortran'] X,
                                       np.ndarray[floating, ndim=2] Y,
                                       int max_iter, floating tol, object rng,
                                       bint random=0):
    """Cython version of the coordinate descent algorithm
        for Elastic-Net mult-task regression

        We minimize

        (1/2) * norm(y - X w, 2)^2 + l1_reg ||w||_21 + (1/2) * l2_reg norm(w, 2)^2

    """

    if floating is float:
        dtype = np.float32
    else:
        dtype = np.float64

    # get the data information into easy vars
    cdef unsigned int n_samples = X.shape[0]
    cdef unsigned int n_features = X.shape[1]
    cdef unsigned int n_tasks = Y.shape[1]

    # to store XtA
    cdef floating[:, ::1] XtA = np.zeros((n_features, n_tasks), dtype=dtype)
    cdef floating XtA_axis1norm
    cdef floating dual_norm_XtA

    # initial value of the residuals
    cdef floating[:, ::1] R = np.zeros((n_samples, n_tasks), dtype=dtype)

    cdef floating[:] norm_cols_X = np.zeros(n_features, dtype=dtype)
    cdef floating[::1] tmp = np.zeros(n_tasks, dtype=dtype)
    cdef floating[:] w_ii = np.zeros(n_tasks, dtype=dtype)
    cdef floating d_w_max
    cdef floating w_max
    cdef floating d_w_ii
    cdef floating nn
    cdef floating W_ii_abs_max
    cdef floating gap = tol + 1.0
    cdef floating d_w_tol = tol
    cdef floating R_norm
    cdef floating w_norm
    cdef floating ry_sum
    cdef floating l21_norm
    cdef unsigned int ii
    cdef unsigned int jj
    cdef unsigned int n_iter = 0
    cdef unsigned int f_iter
    cdef UINT32_t rand_r_state_seed = rng.randint(0, RAND_R_MAX)
    cdef UINT32_t* rand_r_state = &rand_r_state_seed

    cdef floating* X_ptr = &X[0, 0]
    cdef floating* W_ptr = &W[0, 0]
    cdef floating* Y_ptr = &Y[0, 0]
    cdef floating* wii_ptr = &w_ii[0]

    if l1_reg == 0:
        warnings.warn("Coordinate descent with l1_reg=0 may lead to unexpected"
            " results and is discouraged.")

    with nogil:
        # norm_cols_X = (np.asarray(X) ** 2).sum(axis=0)
        for ii in range(n_features):
            for jj in range(n_samples):
                norm_cols_X[ii] += X[jj, ii] ** 2

        # R = Y - np.dot(X, W.T)
        for ii in range(n_samples):
            for jj in range(n_tasks):
                R[ii, jj] = Y[ii, jj] - (
                    _dot(n_features, X_ptr + ii, n_samples, W_ptr + jj, n_tasks)
                    )

        # tol = tol * linalg.norm(Y, ord='fro') ** 2
        tol = tol * _nrm2(n_samples * n_tasks, Y_ptr, 1) ** 2

        for n_iter in range(max_iter):
            w_max = 0.0
            d_w_max = 0.0
            for f_iter in range(n_features):  # Loop over coordinates
                if random:
                    ii = rand_int(n_features, rand_r_state)
                else:
                    ii = f_iter

                if norm_cols_X[ii] == 0.0:
                    continue

                # w_ii = W[:, ii] # Store previous value
                _copy(n_tasks, W_ptr + ii * n_tasks, 1, wii_ptr, 1)

                # if np.sum(w_ii ** 2) != 0.0:  # can do better
                if _nrm2(n_tasks, wii_ptr, 1) != 0.0:
                    # R += np.dot(X[:, ii][:, None], w_ii[None, :]) # rank 1 update
                    _ger(RowMajor, n_samples, n_tasks, 1.0,
                         X_ptr + ii * n_samples, 1,
                         wii_ptr, 1, &R[0, 0], n_tasks)

                # tmp = np.dot(X[:, ii][None, :], R).ravel()
                _gemv(RowMajor, Trans, n_samples, n_tasks, 1.0, &R[0, 0],
                      n_tasks, X_ptr + ii * n_samples, 1, 0.0, &tmp[0], 1)

                # nn = sqrt(np.sum(tmp ** 2))
                nn = _nrm2(n_tasks, &tmp[0], 1)

                # W[:, ii] = tmp * fmax(1. - l1_reg / nn, 0) / (norm_cols_X[ii] + l2_reg)
                _copy(n_tasks, &tmp[0], 1, W_ptr + ii * n_tasks, 1)
                _scal(n_tasks, fmax(1. - l1_reg / nn, 0) / (norm_cols_X[ii] + l2_reg),
                      W_ptr + ii * n_tasks, 1)

                # if np.sum(W[:, ii] ** 2) != 0.0:  # can do better
                if _nrm2(n_tasks, W_ptr + ii * n_tasks, 1) != 0.0:
                    # R -= np.dot(X[:, ii][:, None], W[:, ii][None, :])
                    # Update residual : rank 1 update
                    _ger(RowMajor, n_samples, n_tasks, -1.0,
                         X_ptr + ii * n_samples, 1, W_ptr + ii * n_tasks, 1,
                         &R[0, 0], n_tasks)

                # update the maximum absolute coefficient update
                d_w_ii = diff_abs_max(n_tasks, W_ptr + ii * n_tasks, wii_ptr)

                if d_w_ii > d_w_max:
                    d_w_max = d_w_ii

                W_ii_abs_max = abs_max(n_tasks, W_ptr + ii * n_tasks)
                if W_ii_abs_max > w_max:
                    w_max = W_ii_abs_max

            if w_max == 0.0 or d_w_max / w_max < d_w_tol or n_iter == max_iter - 1:
                # the biggest coordinate update of this iteration was smaller than
                # the tolerance: check the duality gap as ultimate stopping
                # criterion

                # XtA = np.dot(X.T, R) - l2_reg * W.T
                for ii in range(n_features):
                    for jj in range(n_tasks):
                        XtA[ii, jj] = _dot(
                            n_samples, X_ptr + ii * n_samples, 1,
                            &R[0, 0] + jj, n_tasks
                            ) - l2_reg * W[jj, ii]

                # dual_norm_XtA = np.max(np.sqrt(np.sum(XtA ** 2, axis=1)))
                dual_norm_XtA = 0.0
                for ii in range(n_features):
                    # np.sqrt(np.sum(XtA ** 2, axis=1))
                    XtA_axis1norm = _nrm2(n_tasks,
                                          &XtA[0, 0] + ii * n_tasks, 1)
                    if XtA_axis1norm > dual_norm_XtA:
                        dual_norm_XtA = XtA_axis1norm

                # TODO: use squared L2 norm directly
                # R_norm = linalg.norm(R, ord='fro')
                # w_norm = linalg.norm(W, ord='fro')
                R_norm = _nrm2(n_samples * n_tasks, &R[0, 0], 1)
                w_norm = _nrm2(n_features * n_tasks, W_ptr, 1)
                if (dual_norm_XtA > l1_reg):
                    const =  l1_reg / dual_norm_XtA
                    A_norm = R_norm * const
                    gap = 0.5 * (R_norm ** 2 + A_norm ** 2)
                else:
                    const = 1.0
                    gap = R_norm ** 2

                # ry_sum = np.sum(R * y)
                ry_sum = 0.0
                for ii in range(n_samples):
                    for jj in range(n_tasks):
                        ry_sum += R[ii, jj] * Y[ii, jj]

                # l21_norm = np.sqrt(np.sum(W ** 2, axis=0)).sum()
                l21_norm = 0.0
                for ii in range(n_features):
                    # np.sqrt(np.sum(W ** 2, axis=0))
                    l21_norm += _nrm2(n_tasks, W_ptr + n_tasks * ii, 1)

                gap += l1_reg * l21_norm - const * ry_sum + \
                     0.5 * l2_reg * (1 + const ** 2) * (w_norm ** 2)

                if gap < tol:
                    # return if we reached desired tolerance
                    break
        else:
            # for/else, runs if for doesn't end with a `break`
            with gil:
                warnings.warn("Objective did not converge. You might want to "
                              "increase the number of iterations. Duality "
                              "gap: {}, tolerance: {}".format(gap, tol),
                              ConvergenceWarning)

    return np.asarray(W), gap, tol, n_iter + 1
