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core.py
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core.py
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from database import *
import demjson
import numpy as np
from model_manager import Model
import pickle
import math
def get_max_login_id():
q = "select max(login_id) as max from user_login"
res = select(q)
print(res[0]['max'])
if res:
return res[0]['max']
else:
return 0
def create_matrix():
max_id = get_max_login_id()
matrix = []
for i in range(0,max_id+1):
row = []
for j in range(0,max_id+1):
m = Model(i,j)
row.append(m)
matrix.append(row)
for i in range(0,max_id+1):
for j in range(0,max_id+1):
matrix[j][i] = matrix[i][j]
return matrix
def pre_process_features(features):
# print(features)
temp = []
for f in features:
if len(f) == 6 and None not in f:
temp.append(f)
if temp:
temp = temp / np.max(temp)
temp = np.asarray(temp)
return np.asarray(features)
def train_matrix(matrix,user1,user2):
user_1_id = user1['login_id']
user_2_id = user2['login_id']
# print((user1['features']))
# print((user2['features']))
user_1_features = pre_process_features(demjson.decode(user1['features']))
user_2_features = pre_process_features(demjson.decode(user2['features']))
user_1_op = np.asarray([user_1_id] * user_1_features.shape[0])
user_2_op = np.asarray([user_2_id] * user_2_features.shape[0])
# X_train = np.append(user_1_features,user_2_features,axis=0)
# Y_train = np.concatenate((user_1_op,user_2_op),axis=0)
matrix[user_1_id][user_2_id].train(user_1_features,user_2_features,user_1_op,user_2_op)
matrix[user_2_id][user_1_id].train(user_1_features,user_2_features,user_1_op,user_2_op)
# print(X_train)
# print(Y_train)
def train():
matrix = create_matrix()
q = "select * from user_login"
res = select(q)
for i in range((len(res))):
for j in range((len(res))):
user1 = res[i]
user2 = res[j]
train_matrix(matrix,user1,user2)
file = open("model.pickle","wb")
pickle.dump(matrix,file)
file.close()
def predict(matrix,id1,id2,features):
# print(features)
if id1 > -1 and id2 > -1:
res = matrix[id2][id1].predict(features)
# print(matrix[id2][id1])
else:
res = -1
# print(res)
# prob = matrix[id2][id1].predict_proba(features)
# print(prob)
return res
def predict_from_array(matrix,array,features):
print(array)
new_layer = []
if len(array) > 1:
for i in range((len(array) - 1)):
user1 = array[i]
user2 = array[i+1]
new_layer.append(predict(matrix,user1,user2,features))
if len(new_layer) == 1:
return new_layer[0]
else:
user1 = array[0]
user2 = array[0]
# print(features)
return predict(matrix,user1,user2,features)
return predict_from_array(matrix,new_layer,features)
def get_login_id(features):
file = open("model.pickle","rb")
matrix = pickle.load(file)
file.close()
features = pre_process_features(demjson.decode(features))
q = "select * from user_login"
res = select(q)
layer = []
for row in res:
layer.append(row['login_id'])
id = predict_from_array(matrix,layer,features)
return id
train()
# features = "[[75,83,187,175,270,112],[83,112,212,241,324,129],[112,91,268,247,359,156],[91,123,228,260,351,137],[123,153,224,254,377,101],[153,139,210,196,349,57],[139,112,242,215,354,103],[112,110,161,159,271,49],[110,103,799,792,902,689],[103,93,178,168,271,75],[93,125,333,365,458,240],[125,126,228,229,354,103]]"
# id = get_login_id(features)
# print(id)