def test_initialise():
I,J,K,L = 5,3,2,4
R = numpy.ones((I,J))
M = numpy.ones((I,J))
lambdaF = 2*numpy.ones((I,K))
lambdaS = 3*numpy.ones((K,L))
lambdaG = 4*numpy.ones((J,L))
alpha, beta = 3, 1
priors = { 'alpha':alpha, 'beta':beta, 'lambdaF':lambdaF, 'lambdaS':lambdaS, 'lambdaG':lambdaG }
# First do a random initialisation - we can then only check whether values are correctly initialised
init_S = 'random'
init_FG = 'random'
BNMTF = bnmtf_gibbs_optimised(R,M,K,L,priors)
BNMTF.initialise(init_S,init_FG)
assert BNMTF.tau >= 0.0
for i,k in itertools.product(xrange(0,I),xrange(0,K)):
assert BNMTF.F[i,k] >= 0.0
for k,l in itertools.product(xrange(0,K),xrange(0,L)):
assert BNMTF.S[k,l] >= 0.0
for j,l in itertools.product(xrange(0,J),xrange(0,L)):
assert BNMTF.G[j,l] >= 0.0
# Initialisation of S using random draws from prior
init_S, init_FG = 'random', 'exp'
BNMTF = bnmtf_gibbs_optimised(R,M,K,L,priors)
BNMTF.initialise(init_S,init_FG)
for i,k in itertools.product(xrange(0,I),xrange(0,K)):
assert BNMTF.F[i,k] == 1./lambdaF[i,k]
for k,l in itertools.product(xrange(0,K),xrange(0,L)):
assert BNMTF.S[k,l] != 1./lambdaS[k,l] # test whether we overwrote the expectation
for j,l in itertools.product(xrange(0,J),xrange(0,L)):
assert BNMTF.G[j,l] == 1./lambdaG[j,l]
# Initialisation of F and G using random draws from prior
init_S, init_FG = 'exp', 'random'
BNMTF = bnmtf_gibbs_optimised(R,M,K,L,priors)
BNMTF.initialise(init_S,init_FG)
for i,k in itertools.product(xrange(0,I),xrange(0,K)):
assert BNMTF.F[i,k] != 1./lambdaF[i,k] # test whether we overwrote the expectation
for k,l in itertools.product(xrange(0,K),xrange(0,L)):
assert BNMTF.S[k,l] == 1./lambdaS[k,l]
for j,l in itertools.product(xrange(0,J),xrange(0,L)):
assert BNMTF.G[j,l] != 1./lambdaG[j,l] # test whether we overwrote the expectation
# Initialisation of F and G using Kmeans
init_S, init_FG = 'exp', 'kmeans'
BNMTF = bnmtf_gibbs_optimised(R,M,K,L,priors)
BNMTF.initialise(init_S,init_FG)
for i,k in itertools.product(xrange(0,I),xrange(0,K)):
assert BNMTF.F[i,k] == 0.2 or BNMTF.F[i,k] == 1.2
for j,l in itertools.product(xrange(0,J),xrange(0,L)):
assert BNMTF.G[j,l] == 0.2 or BNMTF.G[j,l] == 1.2
for k,l in itertools.product(xrange(0,K),xrange(0,L)):
assert BNMTF.S[k,l] == 1./lambdaS[k,l]
def test_compute_statistics():
R = numpy.array([[1, 2], [3, 4]], dtype=float)
M = numpy.array([[1, 1], [0, 1]])
I, J, K, L = 2, 2, 3, 4
lambdaF = 2 * numpy.ones((I, K))
lambdaS = 3 * numpy.ones((K, L))
lambdaG = 4 * numpy.ones((J, L))
alpha, beta = 3, 1
priors = {
'alpha': alpha,
'beta': beta,
'lambdaF': lambdaF,
'lambdaS': lambdaS,
'lambdaG': lambdaG
}
BNMTF = bnmtf_gibbs_optimised(R, M, K, L, priors)
R_pred = numpy.array([[500, 550], [1220, 1342]], dtype=float)
M_pred = numpy.array([[0, 0], [1, 1]])
MSE_pred = (1217**2 + 1338**2) / 2.0
R2_pred = 1. - (1217**2 + 1338**2) / (0.5**2 + 0.5**2) #mean=3.5
Rp_pred = 61. / (math.sqrt(.5) * math.sqrt(7442.)
) #mean=3.5,var=0.5,mean_pred=1281,var_pred=7442,cov=61
assert MSE_pred == BNMTF.compute_MSE(M_pred, R, R_pred)
assert R2_pred == BNMTF.compute_R2(M_pred, R, R_pred)
assert Rp_pred == BNMTF.compute_Rp(M_pred, R, R_pred)
def test_beta_s():
BNMTF = bnmtf_gibbs_optimised(R, M, K, L, priors)
BNMTF.initialise(init_S, init_FG)
BNMTF.tau = 3.
beta_s = beta + .5 * (12 * (
11. / 15.)**2) #F*S = [[1/6+1/6=1/3,..]], F*S*G^T = [[1/15*4=4/15,..]]
assert abs(BNMTF.beta_s() - beta_s) < 0.00000000000001
def test_log_likelihood():
R = numpy.array([[1,2],[3,4]],dtype=float)
M = numpy.array([[1,1],[0,1]])
I, J, K, L = 2, 2, 3, 4
lambdaF = 2*numpy.ones((I,K))
lambdaS = 3*numpy.ones((K,L))
lambdaG = 4*numpy.ones((J,L))
alpha, beta = 3, 1
priors = { 'alpha':alpha, 'beta':beta, 'lambdaF':lambdaF, 'lambdaS':lambdaS, 'lambdaG':lambdaG }
iterations = 10
burnin, thinning = 4, 2
BNMTF = bnmtf_gibbs_optimised(R,M,K,L,priors)
BNMTF.all_F = [numpy.ones((I,K)) for i in range(0,iterations)]
BNMTF.all_S = [2*numpy.ones((K,L)) for i in range(0,iterations)]
BNMTF.all_G = [3*numpy.ones((J,L)) for i in range(0,iterations)]
BNMTF.all_tau = [3. for i in range(0,iterations)]
# expU*expV.T = [[72.]]
log_likelihood = 3./2.*(math.log(3)-math.log(2*math.pi)) - 3./2. * (71**2 + 70**2 + 68**2)
AIC = -2*log_likelihood + 2*(2*3+3*4+2*4)
BIC = -2*log_likelihood + (2*3+3*4+2*4)*math.log(3)
MSE = (71**2+70**2+68**2)/3.
assert log_likelihood == BNMTF.quality('loglikelihood',burnin,thinning)
assert AIC == BNMTF.quality('AIC',burnin,thinning)
assert BIC == BNMTF.quality('BIC',burnin,thinning)
assert MSE == BNMTF.quality('MSE',burnin,thinning)
with pytest.raises(AssertionError) as error:
BNMTF.quality('FAIL',burnin,thinning)
assert str(error.value) == "Unrecognised metric for model quality: FAIL."
def test_approx_expectation():
burn_in = 2
thinning = 3 # so index 2,5,8 -> m=3,m=6,m=9
(I,J,K,L) = (5,3,2,4)
Fs = [numpy.ones((I,K)) * 3*m**2 for m in range(1,10+1)]
Ss = [numpy.ones((K,L)) * 2*m**2 for m in range(1,10+1)]
Gs = [numpy.ones((J,L)) * 1*m**2 for m in range(1,10+1)] #first is 1's, second is 4's, third is 9's, etc.
taus = [m**2 for m in range(1,10+1)]
expected_exp_tau = (9.+36.+81.)/3.
expected_exp_F = numpy.array([[9.+36.+81.,9.+36.+81.],[9.+36.+81.,9.+36.+81.],[9.+36.+81.,9.+36.+81.],[9.+36.+81.,9.+36.+81.],[9.+36.+81.,9.+36.+81.]])
expected_exp_S = numpy.array([[(9.+36.+81.)*(2./3.),(9.+36.+81.)*(2./3.),(9.+36.+81.)*(2./3.),(9.+36.+81.)*(2./3.)],[(9.+36.+81.)*(2./3.),(9.+36.+81.)*(2./3.),(9.+36.+81.)*(2./3.),(9.+36.+81.)*(2./3.)]])
expected_exp_G = numpy.array([[(9.+36.+81.)*(1./3.),(9.+36.+81.)*(1./3.),(9.+36.+81.)*(1./3.),(9.+36.+81.)*(1./3.)],[(9.+36.+81.)*(1./3.),(9.+36.+81.)*(1./3.),(9.+36.+81.)*(1./3.),(9.+36.+81.)*(1./3.)],[(9.+36.+81.)*(1./3.),(9.+36.+81.)*(1./3.),(9.+36.+81.)*(1./3.),(9.+36.+81.)*(1./3.)]])
R = numpy.ones((I,J))
M = numpy.ones((I,J))
lambdaF = 2*numpy.ones((I,K))
lambdaS = 3*numpy.ones((K,L))
lambdaG = 4*numpy.ones((J,L))
alpha, beta = 3, 1
priors = { 'alpha':alpha, 'beta':beta, 'lambdaF':lambdaF, 'lambdaS':lambdaS, 'lambdaG':lambdaG }
BNMTF = bnmtf_gibbs_optimised(R,M,K,L,priors)
BNMTF.all_F = Fs
BNMTF.all_S = Ss
BNMTF.all_G = Gs
BNMTF.all_tau = taus
(exp_F, exp_S, exp_G, exp_tau) = BNMTF.approx_expectation(burn_in,thinning)
assert expected_exp_tau == exp_tau
assert numpy.array_equal(expected_exp_F,exp_F)
assert numpy.array_equal(expected_exp_S,exp_S)
assert numpy.array_equal(expected_exp_G,exp_G)
def test_run():
I,J,K,L = 10,5,3,2
R = numpy.ones((I,J))
M = numpy.ones((I,J))
M[0,0], M[2,2], M[3,1] = 0, 0, 0
lambdaF = 2*numpy.ones((I,K))
lambdaS = 3*numpy.ones((K,L))
lambdaG = 4*numpy.ones((J,L))
alpha, beta = 3, 1
priors = { 'alpha':alpha, 'beta':beta, 'lambdaF':lambdaF, 'lambdaS':lambdaS, 'lambdaG':lambdaG }
init = 'exp' #F=1/2,S=1/3,G=1/4
F_prior = numpy.ones((I,K))/2.
S_prior = numpy.ones((K,L))/3.
G_prior = numpy.ones((J,L))/4.
iterations = 15
BNMTF = bnmtf_gibbs_optimised(R,M,K,L,priors)
BNMTF.initialise(init)
(Fs,Ss,Gs,taus) = BNMTF.run(iterations)
assert BNMTF.all_F.shape == (iterations,I,K)
assert BNMTF.all_S.shape == (iterations,K,L)
assert BNMTF.all_G.shape == (iterations,J,L)
assert BNMTF.all_tau.shape == (iterations,)
for i,k in itertools.product(xrange(0,I),xrange(0,K)):
assert Fs[0,i,k] != F_prior[i,k]
for k,l in itertools.product(xrange(0,K),xrange(0,L)):
assert Ss[0,k,l] != S_prior[k,l]
for j,l in itertools.product(xrange(0,J),xrange(0,L)):
assert Gs[0,j,l] != G_prior[j,l]
assert taus[1] != alpha/float(beta)
def test_tauG():
BNMTF = bnmtf_gibbs_optimised(R,M,K,L,priors)
BNMTF.initialise(init_S,init_FG)
BNMTF.tau = 3.
# F*S = [[1/3]], (F*S)^2 = [[1/9]], sum_i F*S = [[4/9]]
tauG = 3.*numpy.array([[4./9.,4./9.,4./9.,4./9.],[4./9.,4./9.,4./9.,4./9.],[4./9.,4./9.,4./9.,4./9.]])
for j,l in itertools.product(xrange(0,J),xrange(0,L)):
assert BNMTF.tauG(l)[j] == tauG[j,l]
def test_tauS():
BNMTF = bnmtf_gibbs_optimised(R,M,K,L,priors)
BNMTF.initialise(init_S,init_FG)
BNMTF.tau = 3.
# F outer G = [[1/10]], (F outer G)^2 = [[1/100]], sum (F outer G)^2 = [[12/100]]
tauS = 3.*numpy.array([[3./25.,3./25.,3./25.,3./25.],[3./25.,3./25.,3./25.,3./25.]])
for k,l in itertools.product(xrange(0,K),xrange(0,L)):
assert abs(BNMTF.tauS(k,l) - tauS[k,l]) < 0.000000000000001
def test_tauF():
BNMTF = bnmtf_gibbs_optimised(R,M,K,L,priors)
BNMTF.initialise(init_S,init_FG)
BNMTF.tau = 3.
# S*G.T = [[4/15]], (S*G.T)^2 = [[16/225]], sum_j S*G.T = [[32/225,32/225],[48/225,48/225],[32/225,32/225],[32/225,32/225],[48/225,48/225]]
tauF = 3.*numpy.array([[32./225.,32./225.],[48./225.,48./225.],[32./225.,32./225.],[32./225.,32./225.],[48./225.,48./225.]])
for i,k in itertools.product(xrange(0,I),xrange(0,K)):
assert abs(BNMTF.tauF(k)[i] - tauF[i,k]) < 0.000000000000001
def test_predict():
burn_in = 2
thinning = 3 # so index 2,5,8 -> m=3,m=6,m=9
(I, J, K, L) = (5, 3, 2, 4)
Fs = [numpy.ones((I, K)) * 3 * m**2 for m in range(1, 10 + 1)]
Ss = [numpy.ones((K, L)) * 2 * m**2 for m in range(1, 10 + 1)]
Gs = [numpy.ones((J, L)) * 1 * m**2 for m in range(1, 10 + 1)
] #first is 1's, second is 4's, third is 9's, etc.
Fs[2][
0,
0] = 24 #instead of 27 - to ensure we do not get 0 variance in our predictions
taus = [m**2 for m in range(1, 10 + 1)]
R = numpy.array(
[[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12], [13, 14, 15]],
dtype=float)
M = numpy.ones((I, J))
lambdaF = 2 * numpy.ones((I, K))
lambdaS = 3 * numpy.ones((K, L))
lambdaG = 5 * numpy.ones((J, L))
alpha, beta = 3, 1
priors = {
'alpha': alpha,
'beta': beta,
'lambdaF': lambdaF,
'lambdaS': lambdaS,
'lambdaG': lambdaG
}
#expected_exp_F = numpy.array([[125.,126.],[126.,126.],[126.,126.],[126.,126.],[126.,126.]])
#expected_exp_S = numpy.array([[84.,84.,84.,84.],[84.,84.,84.,84.]])
#expected_exp_G = numpy.array([[42.,42.,42.,42.],[42.,42.,42.,42.],[42.,42.,42.,42.]])
#R_pred = numpy.array([[ 3542112., 3542112., 3542112.],[ 3556224., 3556224., 3556224.],[ 3556224., 3556224., 3556224.],[ 3556224., 3556224., 3556224.],[ 3556224., 3556224., 3556224.]])
M_test = numpy.array(
[[0, 0, 1], [0, 1, 0], [0, 0, 0], [1, 1, 0],
[0, 0, 0]]) #R->3,5,10,11, R_pred->3542112,3556224,3556224,3556224
MSE = ((3. - 3542112.)**2 + (5. - 3556224.)**2 + (10. - 3556224.)**2 +
(11. - 3556224.)**2) / 4.
R2 = 1. - ((3. - 3542112.)**2 + (5. - 3556224.)**2 + (10. - 3556224.)**2 +
(11. - 3556224.)**2) / (4.25**2 + 2.25**2 + 2.75**2 + 3.75**2
) #mean=7.25
Rp = 357. / (
math.sqrt(44.75) * math.sqrt(5292.)
) #mean=7.25,var=44.75, mean_pred=3552696,var_pred=5292, corr=(-4.25*-63 + -2.25*21 + 2.75*21 + 3.75*21)
BNMTF = bnmtf_gibbs_optimised(R, M, K, L, priors)
BNMTF.all_F = Fs
BNMTF.all_S = Ss
BNMTF.all_G = Gs
BNMTF.all_tau = taus
performances = BNMTF.predict(M_test, burn_in, thinning)
assert performances['MSE'] == MSE
assert performances['R^2'] == R2
assert performances['Rp'] == Rp
def test_muG():
BNMTF = bnmtf_gibbs_optimised(R,M,K,L,priors)
BNMTF.initialise(init_S,init_FG)
BNMTF.tau = 3.
tauG = 3.*numpy.array([[4./9.,4./9.,4./9.,4./9.],[4./9.,4./9.,4./9.,4./9.],[4./9.,4./9.,4./9.,4./9.]])
# Rij - Fi*S*Gj + Gjl*(Fi*Sl)) = 11/15 + 1/5 * 1/3 = 12/15 = 4/5
# (Rij - Fi*S*Gj + Gjl*(Fi*Sl)) * (Fi*Sl) = 4/5 * 1/3 = 4/15
muG = 1./tauG * ( 3. * numpy.array([[4.*4./15.,4.*4./15.,4.*4./15.,4.*4./15.],[4.*4./15.,4.*4./15.,4.*4./15.,4.*4./15.],[4.*4./15.,4.*4./15.,4.*4./15.,4.*4./15.]]) - lambdaG )
for j,l in itertools.product(xrange(0,J),xrange(0,L)):
assert abs(BNMTF.muG(tauG[:,l],l)[j] - muG[j,l]) < 0.000000000000001
def test_muS():
BNMTF = bnmtf_gibbs_optimised(R,M,K,L,priors)
BNMTF.initialise(init_S,init_FG)
BNMTF.tau = 3.
tauS = 3.*numpy.array([[3./25.,3./25.,3./25.,3./25.],[3./25.,3./25.,3./25.,3./25.]])
# Rij - Fi*S*Gj + Fik*Skl*Gjk = 11/15 + 1/2*1/3*1/5 = 23/30
# (Rij - Fi*S*Gj + Fik*Skl*Gjk) * Fik*Gjk = 23/30 * 1/10 = 23/300
muS = 1./tauS * ( 3. * numpy.array([[12*23./300.,12*23./300.,12*23./300.,12*23./300.],[12*23./300.,12*23./300.,12*23./300.,12*23./300.]]) - lambdaS )
for k,l in itertools.product(xrange(0,K),xrange(0,L)):
assert abs(BNMTF.muS(tauS[k,l],k,l) - muS[k,l]) < 0.000000000000001
def test_muF():
BNMTF = bnmtf_gibbs_optimised(R,M,K,L,priors)
BNMTF.initialise(init_S,init_FG)
BNMTF.tau = 3.
tauF = 3.*numpy.array([[32./225.,32./225.],[48./225.,48./225.],[32./225.,32./225.],[32./225.,32./225.],[48./225.,48./225.]])
# Rij - Fi*S*Gj + Fik(Sk*Gj) = 11/15 + 1/2 * 4/15 = 13/15
# (Rij - Fi*S*Gj + Fik(Sk*Gj)) * (Sk*Gj) = 13/15 * 4/15 = 52/225
muF = 1./tauF * ( 3. * numpy.array([[2*(52./225.),2*(52./225.)],[3*(52./225.),3*(52./225.)],[2*(52./225.),2*(52./225.)],[2*(52./225.),2*(52./225.)],[3*(52./225.),3*(52./225.)]]) - lambdaF )
for i,k in itertools.product(xrange(0,I),xrange(0,K)):
assert abs(BNMTF.muF(tauF[:,k],k)[i] - muF[i,k]) < 0.000000000000001
lambdaG = numpy.ones((J,L))/10.
priors = { 'alpha':alpha, 'beta':beta, 'lambdaF':lambdaF, 'lambdaS':lambdaS, 'lambdaG':lambdaG }
# Load in data
(_,R,M,_,_,_,_) = load_gdsc(standardised=standardised)
# Run the VB algorithm, <repeats> times
times_repeats = []
performances_repeats = []
for i in range(0,repeats):
# Set all the seeds
numpy.random.seed(3)
# Run the classifier
BNMTF = bnmtf_gibbs_optimised(R,M,K,L,priors)
BNMTF.initialise(init_S,init_FG)
BNMTF.run(iterations)
# Extract the performances and timestamps across all iterations
times_repeats.append(BNMTF.all_times)
performances_repeats.append(BNMTF.all_performances)
# Check whether seed worked: all performances should be the same
assert all(numpy.array_equal(performances, performances_repeats[0]) for performances in performances_repeats), \
"Seed went wrong - performances not the same across repeats!"
# Print out the performances, and the average times
gibbs_all_times_average = list(numpy.average(times_repeats, axis=0))
gibbs_all_performances = performances_repeats[0]
print "gibbs_all_times_average = %s" % gibbs_all_times_average
def test_alpha_s():
BNMTF = bnmtf_gibbs_optimised(R, M, K, L, priors)
BNMTF.initialise(init_S, init_FG)
BNMTF.tau = 3.
alpha_s = alpha + 6.
assert BNMTF.alpha_s() == alpha_s
def test_init():
# Test getting an exception when R and M are different sizes, and when R is not a 2D array.
R1 = numpy.ones(3)
M = numpy.ones((2, 3))
I, J, K, L = 5, 3, 1, 2
lambdaF = numpy.ones((I, K))
lambdaS = numpy.ones((K, L))
lambdaG = numpy.ones((J, L))
alpha, beta = 3, 1
priors = {
'alpha': alpha,
'beta': beta,
'lambdaF': lambdaF,
'lambdaS': lambdaS,
'lambdaG': lambdaG
}
with pytest.raises(AssertionError) as error:
bnmtf_gibbs_optimised(R1, M, K, L, priors)
assert str(
error.value
) == "Input matrix R is not a two-dimensional array, but instead 1-dimensional."
R2 = numpy.ones((4, 3, 2))
with pytest.raises(AssertionError) as error:
bnmtf_gibbs_optimised(R2, M, K, L, priors)
assert str(
error.value
) == "Input matrix R is not a two-dimensional array, but instead 3-dimensional."
R3 = numpy.ones((3, 2))
with pytest.raises(AssertionError) as error:
bnmtf_gibbs_optimised(R3, M, K, L, priors)
assert str(
error.value
) == "Input matrix R is not of the same size as the indicator matrix M: (3, 2) and (2, 3) respectively."
# Similarly for lambdaF, lambdaS, lambdaG
I, J, K, L = 2, 3, 1, 2
R4 = numpy.ones((2, 3))
lambdaF = numpy.ones((2 + 1, 1))
priors = {
'alpha': alpha,
'beta': beta,
'lambdaF': lambdaF,
'lambdaS': lambdaS,
'lambdaG': lambdaG
}
with pytest.raises(AssertionError) as error:
bnmtf_gibbs_optimised(R4, M, K, L, priors)
assert str(
error.value
) == "Prior matrix lambdaF has the wrong shape: (3, 1) instead of (2, 1)."
lambdaF = numpy.ones((2, 1))
lambdaS = numpy.ones((1 + 1, 2 + 1))
priors = {
'alpha': alpha,
'beta': beta,
'lambdaF': lambdaF,
'lambdaS': lambdaS,
'lambdaG': lambdaG
}
with pytest.raises(AssertionError) as error:
bnmtf_gibbs_optimised(R4, M, K, L, priors)
assert str(
error.value
) == "Prior matrix lambdaS has the wrong shape: (2, 3) instead of (1, 2)."
lambdaS = numpy.ones((1, 2))
lambdaG = numpy.ones((3, 2 + 1))
priors = {
'alpha': alpha,
'beta': beta,
'lambdaF': lambdaF,
'lambdaS': lambdaS,
'lambdaG': lambdaG
}
with pytest.raises(AssertionError) as error:
bnmtf_gibbs_optimised(R4, M, K, L, priors)
assert str(
error.value
) == "Prior matrix lambdaG has the wrong shape: (3, 3) instead of (3, 2)."
# Test getting an exception if a row or column is entirely unknown
lambdaF = numpy.ones((I, K))
lambdaS = numpy.ones((K, L))
lambdaG = numpy.ones((J, L))
M1 = [[1, 1, 1], [0, 0, 0]]
M2 = [[1, 1, 0], [1, 0, 0]]
priors = {
'alpha': alpha,
'beta': beta,
'lambdaF': lambdaF,
'lambdaS': lambdaS,
'lambdaG': lambdaG
}
with pytest.raises(AssertionError) as error:
bnmtf_gibbs_optimised(R4, M1, K, L, priors)
assert str(error.value) == "Fully unobserved row in R, row 1."
with pytest.raises(AssertionError) as error:
bnmtf_gibbs_optimised(R4, M2, K, L, priors)
assert str(error.value) == "Fully unobserved column in R, column 2."
# Finally, a successful case
I, J, K, L = 3, 2, 2, 2
R5 = 2 * numpy.ones((I, J))
lambdaF = numpy.ones((I, K))
lambdaS = numpy.ones((K, L))
lambdaG = numpy.ones((J, L))
M = numpy.ones((I, J))
priors = {
'alpha': alpha,
'beta': beta,
'lambdaF': lambdaF,
'lambdaS': lambdaS,
'lambdaG': lambdaG
}
BNMTF = bnmtf_gibbs_optimised(R5, M, K, L, priors)
assert numpy.array_equal(BNMTF.R, R5)
assert numpy.array_equal(BNMTF.M, M)
assert BNMTF.I == I
assert BNMTF.J == J
assert BNMTF.K == K
assert BNMTF.L == L
assert BNMTF.size_Omega == I * J
assert BNMTF.alpha == alpha
assert BNMTF.beta == beta
assert numpy.array_equal(BNMTF.lambdaF, lambdaF)
assert numpy.array_equal(BNMTF.lambdaS, lambdaS)
assert numpy.array_equal(BNMTF.lambdaG, lambdaG)
# Test when lambdaF S G are integers
I, J, K, L = 3, 2, 2, 2
R5 = 2 * numpy.ones((I, J))
lambdaF = 3
lambdaS = 4
lambdaG = 5
M = numpy.ones((I, J))
priors = {
'alpha': alpha,
'beta': beta,
'lambdaF': lambdaF,
'lambdaS': lambdaS,
'lambdaG': lambdaG
}
BNMTF = bnmtf_gibbs_optimised(R5, M, K, L, priors)
assert numpy.array_equal(BNMTF.R, R5)
assert numpy.array_equal(BNMTF.M, M)
assert BNMTF.I == I
assert BNMTF.J == J
assert BNMTF.K == K
assert BNMTF.L == L
assert BNMTF.size_Omega == I * J
assert BNMTF.alpha == alpha
assert BNMTF.beta == beta
assert numpy.array_equal(BNMTF.lambdaF, 3 * numpy.ones((I, K)))
assert numpy.array_equal(BNMTF.lambdaS, 4 * numpy.ones((K, L)))
assert numpy.array_equal(BNMTF.lambdaG, 5 * numpy.ones((J, L)))
