comparison Code/eda.py @ 16:68b8b088f50a

Code for pre-training

 Created on Wed Jul 22 17:42:09 2015
 
 @author: paulochiliguano
 """
 
+
+import random
 import numpy as np
 from sklearn import mixture
 
 #User-item dictionary
 users = {"Angelica": {"SOAJJPC12AB017D63F": 3.5, "SOAKIXJ12AC3DF7152": 2.0,

          "Veronica": {"SOAJJPC12AB017D63F": 3.0, "SOAKPFH12A8C13BA4A": 5.0,
                       "SOAGTJW12A6701F1F5": 4.0, "SOAKWCK12A8C139F81": 2.5,
                       "SOAKNZI12A58A79CAC": 3.0}
         }
 
-items = {"SOAJJPC12AB017D63F": [2.5, 4, 3.5, 3, 5, 4, 1],
-         "SOAKIXJ12AC3DF7152": [2, 5, 5, 3, 2, 1, 1],
-         "SOAKPFH12A8C13BA4A": [1, 5, 4, 2, 4, 1, 1],
-         "SOAGTJW12A6701F1F5": [4, 5, 4, 4, 1, 5, 1],
-         "SOAKWCK12A8C139F81": [1, 4, 5, 3.5, 5, 1, 1],
-         "SOAKNZI12A58A79CAC": [1, 5, 3.5, 3, 4, 5, 1],
-         "SOAJZEP12A8C14379B": [5, 5, 4, 2, 1, 1, 1],
-         "SOAHQFM12A8C134B65": [2.5, 4, 4, 1, 1, 1, 1]}
-
+items = {"SOAJJPC12AB017D63F": [2.5, 4, 3.5, 3, 5, 4, 1, 5, 4, 1],
+         "SOAKIXJ12AC3DF7152": [2, 5, 5, 3, 2, 1, 1, 5, 4, 1],
+         "SOAKPFH12A8C13BA4A": [1, 5, 4, 2, 4, 1, 1, 5, 4, 1],
+         "SOAGTJW12A6701F1F5": [4, 5, 4, 4, 1, 5, 1, 5, 4, 1],
+         "SOAKWCK12A8C139F81": [1, 4, 5, 3.5, 5, 1, 1, 5, 4, 1],
+         "SOAKNZI12A58A79CAC": [1, 5, 3.5, 3, 4, 5, 1, 5, 4, 1],
+         "SOAJZEP12A8C14379B": [5, 5, 4, 2, 1, 1, 1, 5, 4, 1],
+         "SOAHQFM12A8C134B65": [2.5, 4, 4, 1, 1, 1, 1, 5, 4, 1]}
+'''
 profile = {"Profile0": [2.5, 4, 3.5, 3, 5, 4, 1],
            "Profile1": [2.5, 4, 3.5, 3, 5, 4, 1],
            "Profile2": [2.5, 4, 3.5, 3, 5, 4, 1],
            "Profile3": [2.5, 4, 3.5, 3, 5, 4, 1],
            "Profile4": [2.5, 4, 3.5, 3, 5, 4, 1],
            "Profile5": [2.5, 4, 3.5, 3, 5, 4, 1],
            "Profile6": [2.5, 4, 3.5, 3, 5, 4, 1],
            "Profile7": [2.5, 4, 3.5, 3, 5, 4, 1]}
-           
-           
+'''
 
+'''
+Generate M individuals uniformly
+'''
+np.random.seed(len(users))
+M = np.random.uniform(1, 5, len(users) * len(items.values()[0]))
+M.shape = (-1, len(items.values()[0]))
+profile = {}
+i = 0
+for row in M.tolist():
+    profile["Profile" + str(i)] = M.tolist()[i]
+    i = i + 1
 
 np.random.seed(1)
 g = mixture.GMM(n_components=7)
 # Generate random observations with two modes centered on 0
 # and 10 to use for training.