refex_features = np.loadtxt(node_feature, delimiter=',')
np.savetxt(out_dir + '/out-' + out_prefix + '-ids.txt',
X=refex_features[:, 0])
actual_fx_matrix = refex_features[:, 1:]
n, f = actual_fx_matrix.shape
print 'Number of Features: ', f
print 'Number of Nodes: ', n
number_bins = int(np.log2(n))
max_roles = min([n, f])
best_G = None
best_F = None
mdlo = mdl.MDL(number_bins)
minimum_description_length = 1e20
min_des_not_changed_counter = 0
sparsity_threshold = 1.0
for rank in xrange(1, max_roles + 1):
snmf = nimfa.Snmf(actual_fx_matrix,
seed="random_vcol",
version='r',
rank=rank,
beta=2.0)
snmf_fit = snmf()
G = np.asarray(snmf_fit.basis())
F = np.asarray(snmf_fit.coef())
code_length_G = mdlo.get_huffman_code_length(G)
code_length_F = mdlo.get_huffman_code_length(F)
def generateTestMDL():
test = mdl.MDL()
test.skinWidth = 8
test.skinHeight = 8
# add some texture/skin data
pixels = bytes([
208,
208,
208,
208,
208,
208,
208,
208,
208,
250,
250,
250,
250,
250,
250,
208,
208,
250,
250,
250,
250,
250,
144,
208,
208,
250,
250,
250,
144,
144,
144,
208,
208,
250,
250,
144,
144,
144,
144,
208,
208,
250,
144,
144,
144,
144,
144,
208,
208,
250,
144,
144,
144,
144,
144,
208,
208,
208,
208,
208,
208,
208,
208,
208,
])
t = mdl.MDL.Texture(pixels)
s = mdl.MDL.Skin([t], [1.0])
#add texture coordinates
test.texCoords.append(mdl.MDL.TextureCoords(False, 0, 0))
test.texCoords.append(mdl.MDL.TextureCoords(False, 1, 0))
test.texCoords.append(mdl.MDL.TextureCoords(False, 0, 1))
test.texCoords.append(mdl.MDL.TextureCoords(False, 0, 0))
test.texCoords.append(mdl.MDL.TextureCoords(False, 1, 0))
test.texCoords.append(mdl.MDL.TextureCoords(False, 0, 1))
test.texCoords.append(mdl.MDL.TextureCoords(False, 0, 0))
test.texCoords.append(mdl.MDL.TextureCoords(False, 1, 0))
# foo
f = mdl.MDL.SimpleFrame()
f.box_min = mdl.MDL.Vertex(0, 0, 0, 0)
f.box_max = mdl.MDL.Vertex(1, 1, 1, 0)
f.name = "test cube"
f.vertices.append(mdl.MDL.Vertex(0, 0, 0, 0))
f.vertices.append(mdl.MDL.Vertex(1, 0, 0, 0))
f.vertices.append(mdl.MDL.Vertex(1, 1, 0, 0))
f.vertices.append(mdl.MDL.Vertex(0, 1, 0, 0))
f.vertices.append(mdl.MDL.Vertex(0, 1, 1, 0))
f.vertices.append(mdl.MDL.Vertex(0, 0, 1, 0))
f.vertices.append(mdl.MDL.Vertex(1, 0, 1, 0))
f.vertices.append(mdl.MDL.Vertex(1, 1, 1, 0))
return test
bins = int(args.bins)
out_prefix = args.output_prefix
fx = features.Features()
full_fx_matrix = fx.prune_matrix(fx.only_riders(graph_file=graph_file,
rider_dir=rider_dir,
bins=bins,
bin_features=False),
max_diff=0.0)
number_nodes = fx.graph.number_of_nodes()
number_bins = int(np.log2(number_nodes))
n, f = full_fx_matrix.shape
mdlo = mdl.MDL(number_bins)
code_length = mdlo.get_huffman_code_length(full_fx_matrix)
model_cost = code_length * (n + f)
print '*' * 50
print 'Base Model Cost: ', model_cost
print '*' * 50
print 'Num Nodes: ', n
print 'Num FX: ', f
primary_ari = []
primary_ari_uniform = []
secondary_ari = []
secondary_ari_uniform = []
model_costs = []
model_costs_uniform = []
help='output dir',
required=True)
args = argument_parser.parse_args()
curr_graph = args.graph
base_fx_dir = args.base_rider_dir
rf_matrix = args.role_feature
rider_dir = args.rider_dir
out_dir = args.output_dir
H = load_role_fx_matrix(rf_matrix)
losses = []
original = []
mdlo = mdl.MDL(15)
for i in xrange(2, 13):
fx = features.Features()
actual_matrix = fx.dyn_rider(curr_graph,
151,
base_fx_dir,
rider_dir + '/' + str(i) + '/',
bins=15)
# W = estimate_W(actual_matrix, H)
# losses.append(mdlo.get_reconstruction_error(actual_matrix, W.dot(H)))
out_prefix = out_dir + '/' + str(i)
fctr = nimfa.mf(actual_matrix, rank=8, method="lsnmf", max_iter=100)
fctr_res = nimfa.mf_run(fctr)
Wa = np.asarray(fctr_res.basis())
