def update_with_reinforcement(feature_ranks):
initial_weights = get_actual_weights()
from copy import deepcopy
backup = deepcopy(initial_weights)
pmf1 = calculate_pmf(np.array(initial_weights))
print "mass fx 1 ", pmf1
feature_ranks = np.array(feature_ranks)
idx = np.argmax(feature_ranks)
val = np.max(np.array(feature_ranks, np.float))
initial_weights[idx] += val
initial_weights[idx] /= 2.0
pmf_updated = calculate_pmf(initial_weights)
print "mass fx after reinforcement ", pmf_updated
if np.sqrt(2) * np.linalg.norm(pmf_updated - pmf1) > 0.02:
print "outlier detected, reverting back learning"
initial_weights = backup
price = initial_weights[0]
area = initial_weights[1]
bedrooms = initial_weights[2]
crime = initial_weights[3]
InitialWeights.objects.all().delete()
ca = InitialWeights(1, price, area, bedrooms, crime)
ca.save()
import os
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "mypp.settings")
from startpp.models import InitialWeights
import xml.etree.ElementTree as ET
price = 4.621621622
area = 4.081081081
bedrooms = 3.837837838
crime = 4.567567568
#InitialWeights.objects.all().delete()
ca = InitialWeights(1, price, area, bedrooms, crime)
ca.save()
