Adds Mark code

group1/Conv_example.py

+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import normalize
+
+from keras.models import Sequential
+from keras.layers import Dense,Dropout,Conv1D, Flatten, MaxPooling1D
+from keras import optimizers
+from keras import backend as K
+
+from sklearn.model_selection import train_test_split
+import sklearn
+from sklearn.metrics import mean_squared_error
+from sklearn.preprocessing import MinMaxScaler
+import os 
+import sys
+
+
+
+df = pd.read_csv("LUCAS.csv",header=0)
+#XN = df[df.columns[6:]].values
+#XD = (XN[:,4:np.shape(XN)[1]-1]-XN[:,5:np.shape(XN)[1]])/2
+
+#X3 = X2[:,np.random.choice(np.arange(np.shape(XN)[1]),10)]
+
+input_data = df[df.columns[4:]].values
+output_data = df[["SOC"]].values
+#out_data = df[df.columns[4:6]].values
+#input_data = XD
+from sklearn.model_selection import train_test_split
+input_data = np.expand_dims(input_data, axis=2)
+
+input_train, input_test, output_train, output_test = train_test_split(input_data, output_data, test_size=0.33, random_state=42)
+
+
+input_shape = np.shape(input_train)
+
+#idx = np.random.permutation(np.shape(input_data)[0])
+#ida = idx[:int(0.5*len(input_data))]
+#idb = idx[int(0.5*len(input_data)):]
+
+#input_train = input_data[ida]
+#input_test = input_data[idb]
+
+#output_train = out_data[ida]
+#output_test = out_data[idb]
+
+
+def coeff_determination(y_true, y_pred):
+    from keras import backend as K
+    SS_res =  K.sum(K.square( y_true-y_pred ))
+    SS_tot = K.sum(K.square( y_true - K.mean(y_true) ) )
+    return ( 1 - SS_res/(SS_tot + K.epsilon()) )
+
+
+
+
+#Build keras NN model
+K.clear_session()
+
+
+    
+	
+model = Sequential()
+model.add(Conv1D(filters=64, kernel_size=27, activation='relu', input_shape = (1000,1)))
+model.add(Conv1D(filters=64, kernel_size=3, activation='relu'))
+model.add(Dropout(0.5))
+model.add(MaxPooling1D(pool_size=2))
+model.add(Flatten())
+model.add(Dense(100, activation='relu'))
+model.add(Dense(1, activation="relu"))
+
+
+
+##model.add(Conv1D(64,2, activation="relu", input_dim=len(input_train[0])))
+#model.add(Conv1D(filters=64, kernel_size=27, activation='relu', input_shape = (1000,1)))
+##model.add(Dense(8, activation="softmax"))
+#model.add(Dropout(0.5))
+#model.add(MaxPooling1D(pool_size=2))
+#model.add(Flatten())
+#model.add(Dense(64, activation="relu"))
+#model.add(Dense(1, activation="relu"))
+
+
+model.compile(optimizer="RMSprop", loss="mean_squared_error",metrics=[coeff_determination])
+
+
+model.fit(input_train,output_train,epochs=200,batch_size=32)
+
+calculated_cal = model.predict(input_train)
+calculated_val = model.predict(input_test)
+
+rmse_train = np.sqrt(mean_squared_error(output_train,calculated_cal))
+rmse_val = np.sqrt(mean_squared_error(output_test,calculated_val))
+R2_train=sklearn.metrics.r2_score(output_train,calculated_cal)
+R2_val=sklearn.metrics.r2_score(output_test,calculated_val)
+
+
+
+print(rmse_train)
+print(rmse_val)
+print(R2_train)
+print(R2_val)
\ No newline at end of file

group1/mds.py

+# -*- coding: utf-8 -*-
+from __future__ import division
+ 
+import numpy as np
+ 
+def cmdscale(D):
+    """                                                                                       
+    Classical multidimensional scaling (MDS)         
+
+    Code by Francis Song (song.francis@gmail.com)
+    http://www.nervouscomputer.com/hfs/cmdscale-in-python/                                    
+                                                                                               
+    Parameters                                                                                
+    ----------                                                                                
+    D : (n, n) array                                                                          
+        Symmetric distance matrix.                                                            
+                                                                                               
+    Returns                                                                                   
+    -------                                                                                   
+    Y : (n, p) array                                                                          
+        Configuration matrix. Each column represents a dimension. Only the                    
+        p dimensions corresponding to positive eigenvalues of B are returned.                 
+        Note that each dimension is only determined up to an overall sign,                    
+        corresponding to a reflection.                                                        
+                                                                                               
+    e : (n,) array                                                                            
+        Eigenvalues of B.                                                                     
+                                                                                               
+    """
+    # Number of points                                                                        
+    n = len(D)
+ 
+    # Centering matrix                                                                        
+    H = np.eye(n) - np.ones((n, n))/n
+ 
+    # YY^T                                                                                    
+    B = -H.dot(D**2).dot(H)/2
+ 
+    # Diagonalize                                                                             
+    evals, evecs = np.linalg.eigh(B)
+ 
+    # Sort by eigenvalue in descending order                                                  
+    idx   = np.argsort(evals)[::-1]
+    evals = evals[idx]
+    evecs = evecs[:,idx]
+ 
+    # Compute the coordinates using positive-eigenvalued components only                      
+    w, = np.where(evals > 0)
+    L  = np.diag(np.sqrt(evals[w]))
+    V  = evecs[:,w]
+    Y  = V.dot(L)
+ 
+    return Y, evals