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Replace deprecated np.mat() with np.asmatrix() #66

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4 changes: 2 additions & 2 deletions nimfa/methods/factorization/bd.py
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Original file line number Diff line number Diff line change
Expand Up @@ -187,10 +187,10 @@ def __init__(self, V, seed=None, W=None, H=None, rank=30, max_iter=30,
nmf_std.Nmf_std.__init__(self, vars())
if self.alpha is None:
self.alpha = sp.csr_matrix((self.V.shape[0], self.rank))
self.alpha = self.alpha.tocsr() if sp.isspmatrix(self.alpha) else np.mat(self.alpha)
self.alpha = self.alpha.tocsr() if sp.isspmatrix(self.alpha) else np.asmatrix(self.alpha)
if self.beta is None:
self.beta = sp.csr_matrix((self.rank, self.V.shape[1]))
self.beta = self.beta.tocsr() if sp.isspmatrix(self.beta) else np.mat(self.beta)
self.beta = self.beta.tocsr() if sp.isspmatrix(self.beta) else np.asmatrix(self.beta)
if self.n_w is None:
self.n_w = np.zeros((self.rank, 1))
if self.n_h is None:
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4 changes: 2 additions & 2 deletions nimfa/methods/factorization/icm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -162,10 +162,10 @@ def __init__(self, V, seed=None, W=None, H=None, H1=None,
nmf_std.Nmf_std.__init__(self, vars())
if self.alpha is None:
self.alpha = sp.rand(self.V.shape[0], self.rank, density=0.8, format='csr')
self.alpha= self.alpha.tocsr() if sp.isspmatrix(self.alpha) else np.mat(self.alpha)
self.alpha= self.alpha.tocsr() if sp.isspmatrix(self.alpha) else np.asmatrix(self.alpha)
if self.beta is None:
self.beta = sp.rand(self.rank, self.V.shape[1], density=0.8, format='csr')
self.beta = self.beta.tocsr() if sp.isspmatrix(self.beta) else np.mat(self.beta)
self.beta = self.beta.tocsr() if sp.isspmatrix(self.beta) else np.asmatrix(self.beta)
self.tracker = mf_track.Mf_track() if self.track_factor and self.n_run > 1 \
or self.track_error else None

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8 changes: 4 additions & 4 deletions nimfa/methods/factorization/lfnmf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -157,8 +157,8 @@ def factorize(self):
for run in range(self.n_run):
self.W, self.H = self.seed.initialize(
self.V, self.rank, self.options)
self.Sw, self.Sb = np.mat(
np.zeros((1, 1))), np.mat(np.zeros((1, 1)))
self.Sw, self.Sb = np.asmatrix(
np.zeros((1, 1))), np.asmatrix(np.zeros((1, 1)))
p_obj = c_obj = sys.float_info.max
best_obj = c_obj if run == 0 else best_obj
iter = 0
Expand Down Expand Up @@ -244,7 +244,7 @@ def update(self):
# update within class scatter and between class
self.Sw = sum(sum(dot(self.H[:, c2m[i][j]] - avgs[i], (self.H[:, c2m[i][j]] - avgs[i]).T)
for j in range(len(c2m[i]))) for i in c2m)
avgs_t = np.mat(np.zeros((self.rank, 1)))
avgs_t = np.asmatrix(np.zeros((self.rank, 1)))
for k in avgs:
avgs_t += avgs[k]
avgs_t /= len(avgs)
Expand All @@ -259,7 +259,7 @@ def _encoding(self, idxH):
c2m.setdefault(idxH[0, i], [])
c2m[idxH[0, i]].append(i)
# compute mean value of class idx in encoding matrix H
avgs.setdefault(idxH[0, i], np.mat(np.zeros((self.rank, 1))))
avgs.setdefault(idxH[0, i], np.asmatrix(np.zeros((self.rank, 1))))
avgs[idxH[0, i]] += self.H[:, i]
for k in avgs:
avgs[k] /= len(c2m[k])
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2 changes: 1 addition & 1 deletion nimfa/methods/factorization/nmf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -303,7 +303,7 @@ def conn(self):
cons = elop(mat1, mat2, eq)
if not hasattr(self, 'consold'):
self.cons = cons
self.consold = np.mat(np.logical_not(cons))
self.consold = np.asmatrix(np.logical_not(cons))
else:
self.consold = self.cons
self.cons = cons
Expand Down
2 changes: 1 addition & 1 deletion nimfa/methods/factorization/pmfcc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -133,7 +133,7 @@ def __init__(self, V, seed=None, W=None, H=None, rank=30, max_iter=30,
self.aseeds = ["random", "fixed", "nndsvd", "random_c", "random_vcol"]
smf.Smf.__init__(self, vars())
if not self.Theta:
self.Theta = np.mat(np.zeros((self.V.shape[1], self.V.shape[1])))
self.Theta = np.asmatrix(np.zeros((self.V.shape[1], self.V.shape[1])))
self.tracker = mf_track.Mf_track() if self.track_factor and self.n_run > 1 \
or self.track_error else None

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4 changes: 2 additions & 2 deletions nimfa/methods/factorization/psmf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -188,8 +188,8 @@ def factorize(self):
self.H = self.V.__class__(
(self.rank, self.V.shape[1]), dtype='d')
else:
self.W = np.mat(np.zeros((self.V.shape[0], self.rank)))
self.H = np.mat(np.zeros((self.rank, self.V.shape[1])))
self.W = np.asmatrix(np.zeros((self.V.shape[0], self.rank)))
self.H = np.asmatrix(np.zeros((self.rank, self.V.shape[1])))
self.s = np.zeros((self.V.shape[0], self.N), int)
self.r = np.zeros((self.V.shape[0], 1), int)
self.psi = np.array(std(self.V, axis=1, ddof=0))
Expand Down
2 changes: 1 addition & 1 deletion nimfa/methods/factorization/sepnmf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -318,7 +318,7 @@ def norm_axis(X, p=None, axis=None):
if sp.isspmatrix(X):
return sla.norm(X, ord=p, axis=axis)
else:
n = nla.norm(np.mat(X), ord=p, axis=axis)
n = nla.norm(np.asmatrix(X), ord=p, axis=axis)
if axis is None:
return n
else:
Expand Down
36 changes: 18 additions & 18 deletions nimfa/methods/factorization/snmf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -183,8 +183,8 @@ def factorize(self):
if sp.isspmatrix(self.H):
self.H = self.H.tolil()
iter = 0
self.idx_w_old = np.mat(np.zeros((self.V.shape[0], 1)))
self.idx_h_old = np.mat(np.zeros((1, self.V.shape[1])))
self.idx_w_old = np.asmatrix(np.zeros((self.V.shape[0], 1)))
self.idx_h_old = np.asmatrix(np.zeros((1, self.V.shape[1])))
c_obj = sys.float_info.max
best_obj = c_obj if run == 0 else best_obj
# count the number of convergence checks that column clusters and
Expand All @@ -200,7 +200,7 @@ def factorize(self):
sp.eye(self.rank, self.rank, format='lil')
else:
self.beta_vec = sqrt(self.beta) * np.ones((1, self.rank))
self.I_k = self.eta * np.mat(np.eye(self.rank))
self.I_k = self.eta * np.asmatrix(np.eye(self.rank))
self.n_restart = 0
if self.callback_init:
self.final_obj = c_obj
Expand Down Expand Up @@ -287,8 +287,8 @@ def update(self):
if self.n_restart >= 100:
raise utils.MFError(
"Too many restarts due to too large beta parameter.")
self.idx_w_old = np.mat(np.zeros((self.V.shape[0], 1)))
self.idx_h_old = np.mat(np.zeros((1, self.V.shape[1])))
self.idx_w_old = np.asmatrix(np.zeros((self.V.shape[0], 1)))
self.idx_h_old = np.asmatrix(np.zeros((1, self.V.shape[1])))
self.inc = 0
self.W, _ = self.seed.initialize(self.V, self.rank, self.options)
# normalize W and convert to lil
Expand Down Expand Up @@ -333,7 +333,7 @@ def objective(self):
resmat1 = elop(self.W, WHHt - VHt + self.eta ** 2 * self.W, min)
res_vec = nz_data(resmat) + nz_data(resmat1)
# L1 norm
self.conv = norm(np.mat(res_vec), 1)
self.conv = norm(np.asmatrix(res_vec), 1)
err_avg = self.conv / len(res_vec)
self.idx_w_old = idx_w
self.idx_h_old = idx_h
Expand Down Expand Up @@ -388,7 +388,7 @@ def _spfcnnls(self, C, A):
# make infeasible solutions feasible (standard NNLS inner loop)
if len(h_set) > 0:
n_h_set = len(h_set)
alpha = np.mat(np.zeros((l_var, n_h_set)))
alpha = np.asmatrix(np.zeros((l_var, n_h_set)))
while len(h_set) > 0 and iter < max_iter:
iter += 1
alpha[:, :n_h_set] = np.Inf
Expand Down Expand Up @@ -465,15 +465,15 @@ def __spcssls(self, CtC, CtA, p_set=None):
# equivalent if CtC is square matrix
for k in range(CtA.shape[1]):
ls = sp.linalg.gmres(CtC, CtA[:, k].toarray())[0]
K[:, k] = sp.lil_matrix(np.mat(ls).T)
K[:, k] = sp.lil_matrix(np.asmatrix(ls).T)
# K = dot(np.linalg.pinv(CtC), CtA)
else:
l_var, p_rhs = p_set.shape
coded_p_set = dot(
sp.lil_matrix(np.mat(2 ** np.array(list(range(l_var - 1, -1, -1))))), p_set)
sp.lil_matrix(np.asmatrix(2 ** np.array(list(range(l_var - 1, -1, -1))))), p_set)
sorted_p_set, sorted_idx_set = sort(coded_p_set.todense())
breaks = diff(np.mat(sorted_p_set))
break_idx = [-1] + find(np.mat(breaks)) + [p_rhs]
breaks = diff(np.asmatrix(sorted_p_set))
break_idx = [-1] + find(np.asmatrix(breaks)) + [p_rhs]
for k in range(len(break_idx) - 1):
cols2solve = sorted_idx_set[
break_idx[k] + 1: break_idx[k + 1] + 1]
Expand All @@ -483,7 +483,7 @@ def __spcssls(self, CtC, CtA, p_set=None):
sol = sp.lil_matrix(K.shape)
for k in range(tmp_ls.shape[1]):
ls = sp.linalg.gmres(CtC[:, vars][vars, :], tmp_ls[:, k].toarray())[0]
sol[:, k] = sp.lil_matrix(np.mat(ls).T)
sol[:, k] = sp.lil_matrix(np.asmatrix(ls).T)
i = 0
for c in cols2solve:
j = 0
Expand Down Expand Up @@ -519,7 +519,7 @@ def _fcnnls(self, C, A):
A = A.todense() if sp.isspmatrix(A) else A
_, l_var = C.shape
p_rhs = A.shape[1]
W = np.mat(np.zeros((l_var, p_rhs)))
W = np.asmatrix(np.zeros((l_var, p_rhs)))
iter = 0
max_iter = 3 * l_var
# precompute parts of pseudoinverse
Expand All @@ -542,7 +542,7 @@ def _fcnnls(self, C, A):
# make infeasible solutions feasible (standard NNLS inner loop)
if len(h_set) > 0:
n_h_set = len(h_set)
alpha = np.mat(np.zeros((l_var, n_h_set)))
alpha = np.asmatrix(np.zeros((l_var, n_h_set)))
while len(h_set) > 0 and iter < max_iter:
iter += 1
alpha[:, :n_h_set] = np.Inf
Expand Down Expand Up @@ -597,18 +597,18 @@ def __cssls(self, CtC, CtA, p_set=None):
Solve the set of equations CtA = CtC * K for variables defined in set p_set
using the fast combinatorial approach (van Benthem and Keenan, 2004).
"""
K = np.mat(np.zeros(CtA.shape))
K = np.asmatrix(np.zeros(CtA.shape))
if p_set is None or p_set.size == 0 or all(p_set):
# equivalent if CtC is square matrix
K = np.linalg.lstsq(CtC, CtA, rcond=-1)[0]
# K = dot(np.linalg.pinv(CtC), CtA)
else:
l_var, p_rhs = p_set.shape
coded_p_set = dot(
np.mat(2 ** np.array(list(range(l_var - 1, -1, -1)))), p_set)
np.asmatrix(2 ** np.array(list(range(l_var - 1, -1, -1)))), p_set)
sorted_p_set, sorted_idx_set = sort(coded_p_set)
breaks = diff(np.mat(sorted_p_set))
break_idx = [-1] + find(np.mat(breaks)) + [p_rhs]
breaks = diff(np.asmatrix(sorted_p_set))
break_idx = [-1] + find(np.asmatrix(breaks)) + [p_rhs]
for k in range(len(break_idx) - 1):
cols2solve = sorted_idx_set[
break_idx[k] + 1: break_idx[k + 1] + 1]
Expand Down
4 changes: 2 additions & 2 deletions nimfa/methods/factorization/snmnmf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -192,10 +192,10 @@ def __init__(self, V, V1, seed=None, W=None, H=None, H1=None,
nmf_mm.Nmf_mm.__init__(self, vars())
if self.A is None:
self.A = sp.csr_matrix((self.V1.shape[1], self.V1.shape[1]))
self.A = self.A.tocsr() if sp.isspmatrix(self.A) else np.mat(self.A)
self.A = self.A.tocsr() if sp.isspmatrix(self.A) else np.asmatrix(self.A)
if self.B is None:
self.B = sp.csr_matrix((self.V.shape[1], self.V1.shape[1]))
self.B = self.B.tocsr() if sp.isspmatrix(self.B) else np.mat(self.B)
self.B = self.B.tocsr() if sp.isspmatrix(self.B) else np.asmatrix(self.B)
self.tracker = mf_track.Mf_track() if self.track_factor and self.n_run > 1 \
or self.track_error else None

Expand Down