# DBGD init
probe_magnitude = 0.5 # just arbitrary number for now..
change_magnitude = 0.01
# L2R init (create Learning2Rank class instance)
l2r = Learning2Rank('../../data/NP2004/Fold1/test.txt', D)
# ###### REMBO + LEROT ####### #
# Step 1
# Generate random matrix
A = crm.random_matrix(D, d)
# Step 2
# Choose bounded region set: y = N x d matrix (the exhaustive search subset)
y = cbr.choose_bounded_region(d, -region_bound, region_bound,
region_bound_stepsize)
y = np.array(y)
# start with random sample
Y = acq.select_random_point(y)
# t starts from 1 to avoid having t+1 all over the code (might confuse us.)
# is our time the length of the queries we do? We could shorten it for testing at least.
for t in range(1, l2r.get_query_length):
# create y_probe based on y_current
y_probe, altered_dimension = get_similar_point(y_current, probe_magnitude,
region_bound)
# prep for L2R: convert to D-dimensional
Ay_current, Ay_probe = l2r.prepare_rankers(A, y_current, y_probe)
D=10 d=3 # Step 1 # Generate random matrix A = crm.random_matrix(D, d) # Step 2 # Choose bounded region set regionBound = math.sqrt(d) regionBoundStepSize = 0.5 #y = N x d matrix (the exhaustive search subset) y = cbr.choose_bounded_region(d, -regionBound, regionBound, regionBoundStepSize) #create artificial data [0...1] #data = cbr.choose_bounded_region(d,0,1,0.5) #y_projected = acq.projection(data,ybest) max_iter = 10 # t starts from 1 to avoid having t+1 all over the code (might confuse us.) for t in range(1, max_iter+1): #number of samples = t for each iteration step. (is that correct?) number_of_samples = t
# DBGD init
probe_magnitude = 0.5 # just arbitrary number for now..
change_magnitude = 0.01
# L2R init (create Learning2Rank class instance)
l2r = Learning2Rank('../../data/NP2004/Fold1/test.txt', D)
# ###### REMBO + LEROT ####### #
# Step 1
# Generate random matrix
A = crm.random_matrix(D, d)
# Step 2
# Choose bounded region set: y = N x d matrix (the exhaustive search subset)
y = cbr.choose_bounded_region(d, -region_bound, region_bound, region_bound_stepsize)
y = np.array(y)
# start with random sample
Y = acq.select_random_point(y)
# t starts from 1 to avoid having t+1 all over the code (might confuse us.)
# is our time the length of the queries we do? We could shorten it for testing at least.
for t in range(1, l2r.get_query_length):
# create y_probe based on y_current
y_probe, altered_dimension = get_similar_point(y_current, probe_magnitude, region_bound)
# prep for L2R: convert to D-dimensional
Ay_current, Ay_probe = l2r.prepare_rankers(A, y_current, y_probe)
# evaluate
import scipy.spatial.distance as sp
D = 10
d = 3
# Step 1
# Generate random matrix
A = crm.random_matrix(D, d)
# Step 2
# Choose bounded region set
regionBound = math.sqrt(d)
regionBoundStepSize = 0.5
#y = N x d matrix (the exhaustive search subset)
y = cbr.choose_bounded_region(d, -regionBound, regionBound,
regionBoundStepSize)
#create artificial data [0...1]
#data = cbr.choose_bounded_region(d,0,1,0.5)
#y_projected = acq.projection(data,ybest)
max_iter = 10
# t starts from 1 to avoid having t+1 all over the code (might confuse us.)
for t in range(1, max_iter + 1):
#number of samples = t for each iteration step. (is that correct?)
number_of_samples = t
#Step 3 : sample out of y , and create Y
Y = acq.select_sample_set(number_of_samples, y)
# L2R init (create Learning2Rank class instance)
l2r = Learning2Rank('../../data/NP2004/Fold1/test.txt', D)
# ###### REMBO + LEROT ALGORITHM START ####### #
# Step 1
# Generate random matrix
# this mu/sigma might be wrong, I don't know.
mu = 0.5
sigma = 1
A = random_matrix2(D, d, mu, sigma)
# Step 2
# Choose bounded region set: y = N x d matrix (the exhaustive search subset)
y = choose_bounded_region(d, min_region_bound, max_region_bound, region_bound_stepsize)
y = np.array(y)
# start with 1 random sample
Y = get_random_point(y)
# time = amount of queries?
for t in range(1, l2r.get_query_length()):
# create y_current / y_probe based on y_current
y_current = Y[len(Y)-1]
y_probe, altered_dimension = get_random_close_point(y_current, exploration_stepsize, min_region_bound, max_region_bound, d)
# prep for L2R: convert to D-dimensional + create ranker objects
ranker_current, ranker_probe = l2r.prepare_rankers(A, y_current, y_probe)
# evaluate
