# dataset
data_filename = '../data/customeraffinity.train'
n_rows = None

# model paprams
steps = 500
#alpha = 0.002
beta = float(0.1)
tol = 0.01 #done
init = 'random'
K = 8
test_size = 0.1

# Results all the dataset
"""
1.7235698542
steps = 500
#alpha = 0.002
beta = float(0.1)
tol = 0.01 #done
init = 'random'
K = 8
test_size = 0.1

1.74329914519
steps = 500
#alpha = 0.002
beta = float(0.1)
tol = 0.01 #done
init = 'random'
K = 5

1.75909983282
steps = 500
#alpha = 0.002
beta = float(0.1)
tol = 0.01 #done
init = 'random'
K = 2

1.75079552004
steps = 500
#alpha = 0.002
beta = float(0.1)
tol = 0.01 #done
init = 'random'
K = 11

1.76255019781
steps = 1000
#alpha = 0.002
beta = float(0.1)
tol = 0.01 #done
init = 'random'
K = 20

1.75080084517
steps = 500 & 1000
#alpha = 0.002
beta = float(0.01)
tol = 0.01 #done
init = 'random'
K = 11

1.75088171217
steps = 500
#alpha = 0.002
beta = float(1)
tol = 0.01 #done
init = 'random'
K = 11



1.75112734369
steps = 1000
#alpha = 0.002
beta = float(0.1)
tol = 0.001 #done
init = 'random'
K = 11

1.75363204646
steps = 1000
#alpha = 0.002
beta = float(0.1)
tol = 0.1 #done
init = 'random'
K = 11
"""

# Results
"""
1.64030554484
steps = 500
#alpha = 0.002
beta = float(0.01)
tol = 0.01 #done
init = 'random'
K = 11

1.64248828074
steps = 500
#alpha = 0.002
beta = float(0.01)
tol = 0.01 #done
init = 'nndsvdar'
K = 11

1.64216173107
steps = 500
#alpha = 0.002
beta = float(0.01)
tol = 0.01 #done
init = 'nndsvd'
K = 11


1.64070450883
steps = 1000
#alpha = 0.002
beta = float(0.01)
tol = 0.001 #done
init = 'random'
K = 11

1.64102633324
steps = 500
#alpha = 0.002
beta = float(0.01)
tol = 0.01 #done
init = 'random'
K = 12

1.64110070428
steps = 500
#alpha = 0.002
beta = float(0.01)
tol = 0.01 #done
init = 'random'
K = 13

1.64143389135
steps = 500
#alpha = 0.002
beta = float(0.01)
tol = 0.01 #done
init = 'random'
K = 10

1.64154500141
steps = 500
#alpha = 0.002
beta = float(0.01)
tol = 0.01 #done
init = 'random'
K = 15

1.6473022811
steps = 500
#alpha = 0.002
beta = float(0.01)
tol = 0.01 #done
init = 'random'
K = 5

1.64448388191
steps = 500
#alpha = 0.002
beta = float(0.01)
tol = 0.01 #done
init = 'random'
K = 20

1.64161229343
steps = 500
#alpha = 0.002
beta = float(0.05) #0.01
tol = 0.01 #done
init = 'random'
K = 15

1.64189910696
steps = 500
#alpha = 0.002
beta = float(0.1) #0.01
tol = 0.01 #done
init = 'random'
K = 15

1.64154547164
steps = 500
alpha = 0.002
beta = float(0.005) #0.01
tol = 0.01
init = 'random'
K = 15

1.64154886009
steps = 500
#alpha = 0.002
beta = float(0.001) #0.01
tol = 0.01 #done
init = 'random'
K = 15

1.64154941312
steps = 500
#alpha = 0.002
beta = float(0.0005) #0.01
tol = 0.01 #done
init = 'random'
K = 15

1.64290715181
steps = 500
alpha = 0.002
beta = float(0.02) #0.01
tol = 0.1
init = 'random'
K = 15

1.6415464391

steps = 500
alpha = 0.002
beta = float(0.02) #0.01
tol = 0.01
init = 'random'
K = 15

1.64261714611

steps = 50
alpha = 0.002
beta = float(0.02) #0.01
tol = 0.001
init = 'random'
K = 15

1.64171074252
steps = 500
alpha = 0.002
beta = float(0.02) #0.01
tol = 0.001
init = 'random'
K = 15

1.64171074252
steps = 1000
alpha = 0.002
beta = float(0.02) #0.01
tol = 0.001
init = 'random'
K = 15

1.64317847236
steps = 500
alpha = 0.002
beta = float(0.02) #0.01
tol = 0.0001
init = 'random'
K = 15
"""

# dataset
data_filename = '../data/customeraffinity.train'
n_rows = None

# model paprams
steps = 500
#alpha = 0.002
beta = float(0.1)
tol = 0.01 #done
init = 'random'
K = 8
test_size = 0.1

# Results all the dataset
"""
1.7235698542
steps = 500
#alpha = 0.002
beta = float(0.1)
tol = 0.01 #done
init = 'random'
K = 8
test_size = 0.1

1.74329914519
steps = 500
#alpha = 0.002
beta = float(0.1)
tol = 0.01 #done
init = 'random'
K = 5

1.75909983282
steps = 500
#alpha = 0.002
beta = float(0.1)
tol = 0.01 #done
init = 'random'
K = 2

1.75079552004
steps = 500
#alpha = 0.002
beta = float(0.1)
tol = 0.01 #done
init = 'random'
K = 11

1.76255019781
steps = 1000
#alpha = 0.002
beta = float(0.1)
tol = 0.01 #done
init = 'random'
K = 20

1.75080084517
steps = 500 & 1000
#alpha = 0.002
beta = float(0.01)
tol = 0.01 #done
init = 'random'
K = 11

1.75088171217
steps = 500
#alpha = 0.002
beta = float(1)
tol = 0.01 #done
init = 'random'
K = 11



1.75112734369
steps = 1000
#alpha = 0.002
beta = float(0.1)
tol = 0.001 #done
init = 'random'
K = 11

1.75363204646
steps = 1000
#alpha = 0.002
beta = float(0.1)
tol = 0.1 #done
init = 'random'
K = 11
"""

# Results
"""
1.64030554484
steps = 500
#alpha = 0.002
beta = float(0.01)
tol = 0.01 #done
init = 'random'
K = 11

1.64248828074
steps = 500
#alpha = 0.002
beta = float(0.01)
tol = 0.01 #done
init = 'nndsvdar'
K = 11

1.64216173107
steps = 500
#alpha = 0.002
beta = float(0.01)
tol = 0.01 #done
init = 'nndsvd'
K = 11


1.64070450883
steps = 1000
#alpha = 0.002
beta = float(0.01)
tol = 0.001 #done
init = 'random'
K = 11

1.64102633324
steps = 500
#alpha = 0.002
beta = float(0.01)
tol = 0.01 #done
init = 'random'
K = 12

1.64110070428
steps = 500
#alpha = 0.002
beta = float(0.01)
tol = 0.01 #done
init = 'random'
K = 13

1.64143389135
steps = 500
#alpha = 0.002
beta = float(0.01)
tol = 0.01 #done
init = 'random'
K = 10

1.64154500141
steps = 500
#alpha = 0.002
beta = float(0.01)
tol = 0.01 #done
init = 'random'
K = 15

1.6473022811
steps = 500
#alpha = 0.002
beta = float(0.01)
tol = 0.01 #done
init = 'random'
K = 5

1.64448388191
steps = 500
#alpha = 0.002
beta = float(0.01)
tol = 0.01 #done
init = 'random'
K = 20

1.64161229343
steps = 500
#alpha = 0.002
beta = float(0.05) #0.01
tol = 0.01 #done
init = 'random'
K = 15

1.64189910696
steps = 500
#alpha = 0.002
beta = float(0.1) #0.01
tol = 0.01 #done
init = 'random'
K = 15

1.64154547164
steps = 500
alpha = 0.002
beta = float(0.005) #0.01
tol = 0.01
init = 'random'
K = 15

1.64154886009
steps = 500
#alpha = 0.002
beta = float(0.001) #0.01
tol = 0.01 #done
init = 'random'
K = 15

1.64154941312
steps = 500
#alpha = 0.002
beta = float(0.0005) #0.01
tol = 0.01 #done
init = 'random'
K = 15

1.64290715181
steps = 500
alpha = 0.002
beta = float(0.02) #0.01
tol = 0.1
init = 'random'
K = 15

1.6415464391

steps = 500
alpha = 0.002
beta = float(0.02) #0.01
tol = 0.01
init = 'random'
K = 15

1.64261714611

steps = 50
alpha = 0.002
beta = float(0.02) #0.01
tol = 0.001
init = 'random'
K = 15

1.64171074252
steps = 500
alpha = 0.002
beta = float(0.02) #0.01
tol = 0.001
init = 'random'
K = 15

1.64171074252
steps = 1000
alpha = 0.002
beta = float(0.02) #0.01
tol = 0.001
init = 'random'
K = 15

1.64317847236
steps = 500
alpha = 0.002
beta = float(0.02) #0.01
tol = 0.0001
init = 'random'
K = 15
"""

# dataset # model paprams #alpha = 0.002 #done # Results all the dataset 1.7235698542 steps = 500 #alpha = 0.002 beta = float(0.1) tol = 0.01 #done init = 'random' K = 8 test_size = 0.1  1.74329914519 steps = 500 #alpha = 0.002 beta = float(0.1) tol = 0.01 #done init = 'random' K = 5  1.75909983282 steps = 500 #alpha = 0.002 beta = float(0.1) tol = 0.01 #done init = 'random' K = 2  1.75079552004 steps = 500 #alpha = 0.002 beta = float(0.1) tol = 0.01 #done init = 'random' K = 11  1.76255019781 steps = 1000 #alpha = 0.002 beta = float(0.1) tol = 0.01 #done init = 'random' K = 20  1.75080084517 steps = 500 & 1000 #alpha = 0.002 beta = float(0.01) tol = 0.01 #done init = 'random' K = 11  1.75088171217 steps = 500 #alpha = 0.002 beta = float(1) tol = 0.01 #done init = 'random' K = 11    1.75112734369 steps = 1000 #alpha = 0.002 beta = float(0.1) tol = 0.001 #done init = 'random' K = 11  1.75363204646 steps = 1000 #alpha = 0.002 beta = float(0.1) tol = 0.1 #done init = 'random' K = 11 # Results 1.64030554484 steps = 500 #alpha = 0.002 beta = float(0.01) tol = 0.01 #done init = 'random' K = 11  1.64248828074 steps = 500 #alpha = 0.002 beta = float(0.01) tol = 0.01 #done init = 'nndsvdar' K = 11  1.64216173107 steps = 500 #alpha = 0.002 beta = float(0.01) tol = 0.01 #done init = 'nndsvd' K = 11   1.64070450883 steps = 1000 #alpha = 0.002 beta = float(0.01) tol = 0.001 #done init = 'random' K = 11  1.64102633324 steps = 500 #alpha = 0.002 beta = float(0.01) tol = 0.01 #done init = 'random' K = 12  1.64110070428 steps = 500 #alpha = 0.002 beta = float(0.01) tol = 0.01 #done init = 'random' K = 13  1.64143389135 steps = 500 #alpha = 0.002 beta = float(0.01) tol = 0.01 #done init = 'random' K = 10  1.64154500141 steps = 500 #alpha = 0.002 beta = float(0.01) tol = 0.01 #done init = 'random' K = 15  1.6473022811 steps = 500 #alpha = 0.002 beta = float(0.01) tol = 0.01 #done init = 'random' K = 5  1.64448388191 steps = 500 #alpha = 0.002 beta = float(0.01) tol = 0.01 #done init = 'random' K = 20  1.64161229343 steps = 500 #alpha = 0.002 beta = float(0.05) #0.01 tol = 0.01 #done init = 'random' K = 15  1.64189910696 steps = 500 #alpha = 0.002 beta = float(0.1) #0.01 tol = 0.01 #done init = 'random' K = 15  1.64154547164 steps = 500 alpha = 0.002 beta = float(0.005) #0.01 tol = 0.01 init = 'random' K = 15  1.64154886009 steps = 500 #alpha = 0.002 beta = float(0.001) #0.01 tol = 0.01 #done init = 'random' K = 15  1.64154941312 steps = 500 #alpha = 0.002 beta = float(0.0005) #0.01 tol = 0.01 #done init = 'random' K = 15  1.64290715181 steps = 500 alpha = 0.002 beta = float(0.02) #0.01 tol = 0.1 init = 'random' K = 15  1.6415464391  steps = 500 alpha = 0.002 beta = float(0.02) #0.01 tol = 0.01 init = 'random' K = 15  1.64261714611  steps = 50 alpha = 0.002 beta = float(0.02) #0.01 tol = 0.001 init = 'random' K = 15  1.64171074252 steps = 500 alpha = 0.002 beta = float(0.02) #0.01 tol = 0.001 init = 'random' K = 15  1.64171074252 steps = 1000 alpha = 0.002 beta = float(0.02) #0.01 tol = 0.001 init = 'random' K = 15  1.64317847236 steps = 500 alpha = 0.002 beta = float(0.02) #0.01 tol = 0.0001 init = 'random' K = 15