comparison Code/eda.py @ 15:2e3c57fba632

Convolutional Neural Network code
Scratch for Estimation of Distribution Algorithm

+# -*- coding: utf-8 -*-
+"""
+Created on Wed Jul 22 17:42:09 2015
+
+@author: paulochiliguano
+"""
+
+import numpy as np
+from sklearn import mixture
+
+#User-item dictionary
+users = {"Angelica": {"SOAJJPC12AB017D63F": 3.5, "SOAKIXJ12AC3DF7152": 2.0,
+                      "SOAKPFH12A8C13BA4A": 4.5, "SOAGTJW12A6701F1F5": 5.0,
+                      "SOAKWCK12A8C139F81": 1.5, "SOAKNZI12A58A79CAC": 2.5,
+                      "SOAJZEP12A8C14379B": 2.0},
+         "Bill":{"SOAJJPC12AB017D63F": 2.0, "SOAKIXJ12AC3DF7152": 3.5,
+                 "SOAHQFM12A8C134B65": 4.0, "SOAGTJW12A6701F1F5": 2.0,
+                 "SOAKWCK12A8C139F81": 3.5, "SOAJZEP12A8C14379B": 3.0},
+         "Chan": {"SOAJJPC12AB017D63F": 5.0, "SOAKIXJ12AC3DF7152": 1.0,
+                  "SOAHQFM12A8C134B65": 1.0, "SOAKPFH12A8C13BA4A": 3.0,
+                  "SOAGTJW12A6701F1F5": 5, "SOAKWCK12A8C139F81": 1.0},
+         "Dan": {"SOAJJPC12AB017D63F": 3.0, "SOAKIXJ12AC3DF7152": 4.0,
+                 "SOAHQFM12A8C134B65": 4.5, "SOAGTJW12A6701F1F5": 3.0,
+                 "SOAKWCK12A8C139F81": 4.5, "SOAKNZI12A58A79CAC": 4.0,
+                 "SOAJZEP12A8C14379B": 2.0},
+         "Hailey": {"SOAKIXJ12AC3DF7152": 4.0, "SOAHQFM12A8C134B65": 1.0,
+                    "SOAKPFH12A8C13BA4A": 4.0, "SOAKNZI12A58A79CAC": 4.0,
+                    "SOAJZEP12A8C14379B": 1.0},
+         "Jordyn":  {"SOAKIXJ12AC3DF7152": 4.5, "SOAHQFM12A8C134B65": 4.0,
+                     "SOAKPFH12A8C13BA4A": 5.0, "SOAGTJW12A6701F1F5": 5.0,
+                     "SOAKWCK12A8C139F81": 4.5, "SOAKNZI12A58A79CAC": 4.0,
+                     "SOAJZEP12A8C14379B": 4.0},
+         "Sam": {"SOAJJPC12AB017D63F": 5.0, "SOAKIXJ12AC3DF7152": 2.0,
+                 "SOAKPFH12A8C13BA4A": 3.0, "SOAGTJW12A6701F1F5": 5.0,
+                 "SOAKWCK12A8C139F81": 4.0, "SOAKNZI12A58A79CAC": 5.0},
+         "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]}
+
+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]}
+           
+           
+
+
+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.
+obs = np.concatenate((np.random.randn(100, 1), 10 + np.random.randn(300, 1)))
+g.fit(obs) 
+np.round(g.weights_, 2)
+np.round(g.means_, 2)
+np.round(g.covars_, 2) 
+g.predict([[0], [2], [9], [10]]) 
+np.round(g.score([[0], [2], [9], [10]]), 2)
+# Refit the model on new data (initial parameters remain the
+# same), this time with an even split between the two modes.
+g.fit(20 * [[0]] +  20 * [[10]]) 
+np.round(g.weights_, 2)