def compute_ghcm_mdt_one_user(user_id):
file_name = "ghcm_mdt_user_" + str(user_id)
print "loading matrix user " + str(user_id) + "..."
[rfvdata, featuresnames, valuesnames,
recordsdates] = RVFDataExtractor.load_rvf(user_id)
print "values" + JsonUtils.dict_as_json_str(valuesnames)
print "data" + JsonUtils.dict_as_json_str(rfvdata[0])
vocab_size = [len(valuesnames[f]) for f in range(len(valuesnames.keys()))]
print "user " + str(user_id) + " has " + str(
len(featuresnames)) + " features and " + str(len(rfvdata)) + " records"
print "features names"
print featuresnames
print "values" + JsonUtils.dict_as_json_str(valuesnames)
for k in [10, 20, 30]:
#compute the ghcm_mdt
ghcm_mdt_comp = GHCM_MDTComputer(rfvdata, k, vocab_size)
print "computing SVD for user " + str(user_id) + "..."
ghcm_mdt_comp.compute()
print "constructing interpretable output for user " + str(
user_id) + "..."
ghcm_mdt_comp.construct_rows_interpretable_output(
featuresnames, valuesnames, disp_m)
r_output = ghcm_mdt_comp.rows_interpretable_output
#write the result
print "writing SVD result for user " + str(user_id) + "..."
JsonLogsFileWriter.write(r_output, file_name)
def train(self, train_data_rfv):
self.ghcm_mdt = GHCM_MDTComputer(train_data_rfv, self.k,
self.vocab_size)
self.ghcm_mdt.gibbs_iter = self.train_gibbsiter
self.ghcm_mdt.hyperparams_iter = self.train_hyperparamsiter
self.ghcm_mdt.hyper_parameter_number_of_samples_to_average = self.nb_averages
self.ghcm_mdt.compute()
def train(self, train_data_rfv):
self.ghcm_mdt = GHCM_MDTComputer(train_data_rfv, self.k,
self.vocab_size)
self.ghcm_mdt.gibbs_iter = self.train_gibbsiter
self.ghcm_mdt.hyperparams_iter = self.train_hyperparamsiter
self.ghcm_mdt.compute()
