def __init__(self,
d,ranker_arg_str,
ties,
feature_count,
init=None,
sample=None):
self.A = random_matrix(feature_count, d)
self.feature_count = feature_count
ranking_model_str = "ranker.model.LinearREMBO"
for arg in ranker_arg_str:
if arg.startswith("ranker.model"):
ranking_model_str = arg
else:
self.ranker_type = float(arg)
self.ranking_model = get_class(ranking_model_str)(d)
self.sample = getattr(__import__("utils"), sample)
self.ties = ties
self.w = self.ranking_model.initialize_weights(init,self.A,d)
def __init__(self,
d,
ranker_arg_str,
ties,
feature_count,
init=None,
sample=None):
self.A = random_matrix(feature_count, d)
self.feature_count = feature_count
ranking_model_str = "ranker.model.LinearREMBO"
for arg in ranker_arg_str:
if arg.startswith("ranker.model"):
ranking_model_str = arg
else:
self.ranker_type = float(arg)
self.ranking_model = get_class(ranking_model_str)(d)
self.sample = getattr(__import__("utils"), sample)
self.ties = ties
self.w = self.ranking_model.initialize_weights(init, self.A, d)
d = 3
region_bound = math.sqrt(d)
region_bound_stepsize = 0.5
# 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,
import numpy as np import math import parisFunctions as pf import acquisition_function as acq import create_random_matrix as crm import choose_bounded_region as cbr 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)
