def generate_dataset(I, J, K, lambdaU, lambdaV, tau): # Generate U, V U = numpy.zeros((I, K)) for i, k in itertools.product(xrange(0, I), xrange(0, K)): U[i, k] = exponential_draw(lambdaU[i, k]) V = numpy.zeros((J, K)) for j, k in itertools.product(xrange(0, J), xrange(0, K)): V[j, k] = exponential_draw(lambdaV[j, k]) # Generate R true_R = numpy.dot(U, V.T) R = add_noise(true_R, tau) return (U, V, tau, true_R, R)
def generate_dataset(I,J,K,lambdaU,lambdaV,tau): # Generate U, V U = numpy.zeros((I,K)) for i,k in itertools.product(xrange(0,I),xrange(0,K)): U[i,k] = exponential_draw(lambdaU[i,k]) V = numpy.zeros((J,K)) for j,k in itertools.product(xrange(0,J),xrange(0,K)): V[j,k] = exponential_draw(lambdaV[j,k]) # Generate R true_R = numpy.dot(U,V.T) R = add_noise(true_R,tau) return (U,V,tau,true_R,R)
def generate_dataset(I,J,K,L,lambdaF,lambdaS,lambdaG,tau): # Generate U, V F = numpy.zeros((I,K)) for i,k in itertools.product(xrange(0,I),xrange(0,K)): F[i,k] = exponential_draw(lambdaF[i,k]) S = numpy.zeros((K,L)) for k,l in itertools.product(xrange(0,K),xrange(0,L)): S[k,l] = exponential_draw(lambdaS[k,l]) G = numpy.zeros((J,L)) for j,l in itertools.product(xrange(0,J),xrange(0,L)): G[j,l] = exponential_draw(lambdaG[j,l]) # Generate R true_R = numpy.dot(F,numpy.dot(S,G.T)) R = add_noise(true_R,tau) return (F,S,G,tau,true_R,R)
def generate_dataset(I, J, K, L, lambdaF, lambdaS, lambdaG, tau): # Generate U, V F = numpy.zeros((I, K)) for i, k in itertools.product(xrange(0, I), xrange(0, K)): F[i, k] = exponential_draw(lambdaF[i, k]) S = numpy.zeros((K, L)) for k, l in itertools.product(xrange(0, K), xrange(0, L)): S[k, l] = exponential_draw(lambdaS[k, l]) G = numpy.zeros((J, L)) for j, l in itertools.product(xrange(0, J), xrange(0, L)): G[j, l] = exponential_draw(lambdaG[j, l]) # Generate R true_R = numpy.dot(F, numpy.dot(S, G.T)) R = add_noise(true_R, tau) return (F, S, G, tau, true_R, R)