diff --git a/RNNHandler.py b/RNNHandler.py
new file mode 100644
index 0000000..75a5227
--- /dev/null
+++ b/RNNHandler.py
@@ -0,0 +1,126 @@
+import keras.utils
+from keras.models import Sequential, model_from_json, load_model
+from keras.layers import Dense, Activation, SimpleRNN
+from keras.utils.visualize_util import plot
+import keras.utils.np_utils
+from keras.utils.np_utils import to_categorical
+
+import os
+import numpy as np
+import datetime
+import random
+from reader import Reader
+from sklearn import metrics
+import matplotlib.pyplot as plt
+from sklearn import metrics
+
+def MyMetrics(y_true, y_pred):
+	y_pred[y_pred<0.5] = 0
+	y_pred[y_pred>=0.5] = 1
+	if np.count_nonzero(y_pred == 1) == y_pred.shape[0]:
+		y_pred[0]=0
+	if np.count_nonzero(y_pred == 0) == y_pred.shape[0]:
+		y_pred[0]= 1
+	if np.count_nonzero(y_true == 1) == y_true.shape[0]:
+		y_true[0]=0
+	if np.count_nonzero(y_true == 0) == y_true.shape[0]:
+		y_true[0]= 1
+	confusion = metrics.confusion_matrix(y_true, y_pred)
+	TP = confusion[1, 1]
+	TN = confusion[0, 0]
+	FP = confusion[0, 1]
+	FN = confusion[1, 0]
+	precision = TP / (TP + FP)
+	recall = TP / (TP + FN)
+	fscore = 2 * precision * recall / ( precision + recall)
+	return (precision, recall, fscore)
+
+
+class RNNHandler:
+	
+	results_directory = '/home/orestis/net/RNNresults'
+	models_directory = '/home/orestis/net/RNNmodels'
+
+	def __init__(self, model_name, num_categories, loss, optimizer):
+		# GET THE MODEL
+		fp_model = open(os.path.join(self.models_directory, model_name + '.json'), 'r')
+		model_str = fp_model.read()
+		self.model = model_from_json(model_str)
+		self.model.compile(loss=loss, optimizer=optimizer, metrics=['accuracy'])		
+		fp_model.close()
+
+		self.model_name = model_name
+		self.num_categories = num_categories
+		self.loss = loss
+		self.optimizer = optimizer
+		
+		
+	def fit_and_eval(self, x_train, y_train, x_test, y_test, nb_epoch, dataset_name):	#batch_size is always 1 and shuffle is always False, so we don't pass them as parameters
+		self.results_file = os.path.join(self.results_directory, self.model_name + '.' + dataset_name)
+		self.write_result('Model:' + self.model_name + ' Dataset:' + dataset_name + ' Loss:' + self.loss + ' Optimizer:' + self.optimizer + ' Dropout:No')
+		self.write_result('Epoch|Loss|Accuracy|Precision|Recall|Fscore')		
+		res_loss = []
+		res_accuracy = []
+		res_precision = []		
+		res_recall = []
+		res_fscore = []
+		x_train = x_train.reshape(x_train.shape[0], 1, -1)
+		x_test = x_test.reshape(x_test.shape[0], 1, -1)
+		if self.num_categories > 2:
+			y_train = to_categorical(y_train, self.num_categories)
+			y_test = to_categorical(y_test, self.num_categories)
+		for i in range(1, nb_epoch+1):			
+			self.model.fit(x_train, y_train, batch_size=1, nb_epoch=1, shuffle=False)
+			self.model.reset_states()
+			(loss, accuracy) = self.model.evaluate(x_test, y_test, batch_size=1)
+			self.model.reset_states()
+			res_loss.append(loss)
+			res_accuracy.append(accuracy)
+			(precision, recall, fscore) = (0,0,0)
+			if self.num_categories == 2:			
+				y_pred = self.model.predict(x_test, batch_size=1)
+				self.model.reset_states()
+				(precision, recall, fscore) = MyMetrics(y_test, y_pred)
+				res_precision.append(precision)
+				res_recall.append(recall)
+				res_fscore.append(fscore)
+			self.write_result(str(i) +'|'+ str(loss) +'|'+ str(accuracy) +'|'+ str(precision) +'|'+ str(recall) +'|'+ str(fscore))
+
+		return (res_loss, res_accuracy, res_precision, res_recall, res_fscore)
+	
+
+	def write_result(self, text):
+		fp = open(self.results_file, 'a')
+		fp.write(text + '\n')
+		fp.close()
+
+#	def save_weights():
+
+
+
+	@staticmethod
+	def plot_results(title, metric, results):
+		lns = []
+		for k in results.keys():
+			result = results[k]
+			myplot = plt.subplot()
+			myplot.grid(True)
+			myplot.set_xlabel("Epoch Number")
+			myplot.set_ylabel(metric)
+			x_Axis = np.arange(1, len(result)+1)
+			#myplot.xaxis.set_ticks(x_Axis)#np.arange(	1, len(x_Axis)+1, 1))
+			#myplot.set_xticklabels(x_Axis, rotation=0)
+			tokens = k.split('|')
+			loss = tokens[0]
+			if loss=='categorical_crossentropy' or loss=='binary_crossentropy':
+				loss = 'crossentropy'
+			optimizer = tokens[1]
+			line = myplot.plot(x_Axis, result, label = 'loss:' + loss + ' opt:' + optimizer)		
+			lns = lns + line
+		box = myplot.get_position()
+		myplot.set_position([box.x0, box.y0 + box.height * 0.30, box.width, box.height * 0.70])
+		labs = [l.get_label() for l in lns]
+		plt.title(title)
+		lgd = plt.legend(lns, labs, loc='upper center', bbox_to_anchor=(0.5, -0.12), fancybox=True, shadow=True, ncol=2)
+		plt.savefig('/home/orestis/net/RNNresults/' + title + '.png')
+		plt.clf()
diff --git a/RNN_1A.py b/RNN_1A.py
new file mode 100644
index 0000000..924227c
--- /dev/null
+++ b/RNN_1A.py
@@ -0,0 +1,26 @@
+
+from keras.models import Sequential
+from keras.layers import Dense, Activation, SimpleRNN
+from keras.utils.visualize_util import plot
+import os
+import numpy as np
+from reader import Reader
+
+
+length = Reader.getInputShape()
+
+model = Sequential()
+
+#EXPECTS INPUT AS (nb_sample, timesteps, nb_features), where nb_sample=1 (batch_size = 1), timesteps = 1 and nb_features = length
+
+#model.add(Dense(40, input_dim = 12, init='uniform', activation='relu'))
+model.add(SimpleRNN(output_dim=50, input_shape=(1,length), batch_input_shape=(1,1,length), init='uniform', inner_init='uniform', activation='relu', stateful=True))
+model.add(Dense(1, init='uniform', activation = 'sigmoid'))
+
+
+model.summary()
+plot(model, to_file='/home/orestis/net/RNNmodels/RNN_1A.png')
+fp = open('/home/orestis/net/RNNmodels/RNN_1A.json', 'w')
+fp.write(model.to_json())
+fp.close()
+
diff --git a/RNN_1B.py b/RNN_1B.py
new file mode 100644
index 0000000..dd51820
--- /dev/null
+++ b/RNN_1B.py
@@ -0,0 +1,26 @@
+
+from keras.models import Sequential
+from keras.layers import Dense, Activation, SimpleRNN
+from keras.utils.visualize_util import plot
+import os
+import numpy as np
+from reader import Reader
+
+
+length = Reader.getInputShape()
+
+model = Sequential()
+
+#EXPECTS INPUT AS (nb_sample, timesteps, nb_features), where nb_sample=1 (batch_size = 1), timesteps = 1 and nb_features = length
+
+#model.add(Dense(40, input_dim = 12, init='uniform', activation='relu'))
+model.add(SimpleRNN(output_dim=50, input_shape=(1,length), batch_input_shape=(1,1,length), init='uniform', inner_init='uniform', activation='relu', stateful=True))
+model.add(Dense(3, init='uniform', activation = 'softmax'))
+
+
+model.summary()
+plot(model, to_file='/home/orestis/net/RNNmodels/RNN_1B.png')
+fp = open('/home/orestis/net/RNNmodels/RNN_1B.json', 'w')
+fp.write(model.to_json())
+fp.close()
+
diff --git a/RNN_1C.py b/RNN_1C.py
new file mode 100644
index 0000000..46f0918
--- /dev/null
+++ b/RNN_1C.py
@@ -0,0 +1,26 @@
+
+from keras.models import Sequential
+from keras.layers import Dense, Activation, SimpleRNN
+from keras.utils.visualize_util import plot
+import os
+import numpy as np
+from reader import Reader
+
+
+length = Reader.getInputShape()
+
+model = Sequential()
+
+#EXPECTS INPUT AS (nb_sample, timesteps, nb_features), where nb_sample=1 (batch_size = 1), timesteps = 1 and nb_features = length
+
+#model.add(Dense(40, input_dim = 12, init='uniform', activation='relu'))
+model.add(SimpleRNN(output_dim=50, input_shape=(1,length), batch_input_shape=(1,1,length), init='uniform', inner_init='uniform', activation='relu', stateful=True))
+model.add(Dense(5, init='uniform', activation = 'softmax'))
+
+
+model.summary()
+plot(model, to_file='/home/orestis/net/RNNmodels/RNN_1C.png')
+fp = open('/home/orestis/net/RNNmodels/RNN_1C.json', 'w')
+fp.write(model.to_json())
+fp.close()
+
diff --git a/RNN_plot_all.py b/RNN_plot_all.py
new file mode 100644
index 0000000..3d2f9b7
--- /dev/null
+++ b/RNN_plot_all.py
@@ -0,0 +1,85 @@
+import keras.utils
+from keras.models import Sequential, model_from_json, load_model
+from keras.layers import Dense, Activation, SimpleRNN
+from keras.utils.visualize_util import plot
+import keras.utils.np_utils
+from keras.utils.np_utils import to_categorical
+
+import os
+import numpy as np
+import datetime
+import random
+from reader import Reader
+from sklearn import metrics
+import matplotlib.pyplot as plt
+from sklearn import metrics
+import RNNHandler
+
+
+#(1, 'Dataset1', 2, 'RNN_1A')
+#(2, 'Dataset2', 2, 'RNN_1A')
+#(3, 'Dataset3', 2, 'RNN_1A') !! grafiki
+#(4, 'Dataset4', 2, 'RNN_1A') !!! mixed type binary and continous
+#(5, 'Dataset5', 3, 'RNN_1B') +categorical
+dataset_id = 5
+dataset_name = 'Dataset5'
+num_classes = 3
+RNN_name = 'RNN_1B'
+
+num_epochs = 10
+
+(x_train, y_train), (x_test, y_test) = Reader.getDataset(dataset_id)
+x_train = x_train[0:1000,:]
+y_train = y_train[0:1000]
+x_test = x_test[0:1000,:]
+y_test = y_test[0:1000]
+
+results = {}
+for loss in ['mae', 'mse', 'categorical_crossentropy']:	#categorical_crossentropy
+	for optimizer in ['sgd','adagrad', 'rmsprop']:
+		RNNmodel = RNNHandler.RNNHandler(RNN_name, num_classes, loss, optimizer)
+		(res_loss, res_accuracy, res_precision, res_recall, res_fscore) = RNNmodel.fit_and_eval(x_train, y_train, x_test, y_test, num_epochs, dataset_name)
+		if num_classes == 2:		
+			results[loss + '|' + optimizer] = res_fscore
+		else:
+			results[loss + '|' + optimizer] = res_accuracy
+
+title = 'Dataset ' + dataset_name + ', ' + 'Model ' + RNN_name
+metric = 'accuracy'
+if num_classes == 2:
+	metric = 'fscore'
+RNNHandler.RNNHandler.plot_results(title, metric, results)
+
+
+
+#(6, 'Dataset0', 5, 'RNN_1C') +categorical
+dataset_id = 6
+dataset_name = 'Dataset0'
+num_classes = 5
+RNN_name = 'RNN_1C'
+
+num_epochs = 10
+
+fp_logfile = open('/home/orestis/net/debug/logfile', "a")
+reader = Reader(fp_logfile, False)
+(x_train, y_train), (x_test, y_test) = reader.getDataNormalized()
+x_train = x_train[0:1000,:]
+y_train = y_train[0:1000]
+x_test = x_test[0:1000,:]
+y_test = y_test[0:1000]
+results = {}
+for loss in ['mae', 'mse', 'categorical_crossentropy']: 
+	for optimizer in ['sgd','adagrad', 'rmsprop']:
+		RNNmodel = RNNHandler.RNNHandler(RNN_name, num_classes, loss, optimizer)
+		(res_loss, res_accuracy, res_precision, res_recall, res_fscore) = RNNmodel.fit_and_eval(x_train, y_train, x_test, y_test, num_epochs, dataset_name)			
+		if num_classes == 2:		
+			results[loss + '|' + optimizer] = res_fscore
+		else:
+			results[loss + '|' + optimizer] = res_accuracy
+
+title = 'Dataset: ' + dataset_name + ', ' + 'Model: ' + RNN_name
+metric = 'accuracy'
+if num_classes == 2:
+	metric = 'fscore'
+RNNHandler.RNNHandler.plot_results(title, metric, results)
+
diff --git a/RNNmodels/RNN_1.json b/RNNmodels/RNN_1.json
new file mode 100644
index 0000000..2d9ab94
--- /dev/null
+++ b/RNNmodels/RNN_1.json
@@ -0,0 +1 @@
+{"class_name": "Sequential", "config": [{"class_name": "SimpleRNN", "config": {"b_regularizer": null, "U_regularizer": null, "W_regularizer": null, "activation": "relu", "go_backwards": false, "init": "uniform", "inner_init": "uniform", "dropout_U": 0.0, "input_dtype": "float32", "batch_input_shape": [1, 1, 79], "name": "simplernn_1", "return_sequences": false, "dropout_W": 0.0, "unroll": false, "trainable": true, "stateful": true, "consume_less": "cpu", "output_dim": 50}}, {"class_name": "Dense", "config": {"b_regularizer": null, "W_regularizer": null, "activation": "softmax", "activity_regularizer": null, "b_constraint": null, "init": "uniform", "bias": true, "W_constraint": null, "input_dim": null, "output_dim": 3, "trainable": true, "name": "dense_1"}}], "keras_version": "1.1.1"}
\ No newline at end of file
diff --git a/RNNmodels/RNN_1.png b/RNNmodels/RNN_1.png
new file mode 100644
index 0000000..9f62edb
Binary files /dev/null and b/RNNmodels/RNN_1.png differ
diff --git a/RNNmodels/RNN_1A.json b/RNNmodels/RNN_1A.json
new file mode 100644
index 0000000..f2447e1
--- /dev/null
+++ b/RNNmodels/RNN_1A.json
@@ -0,0 +1 @@
+{"keras_version": "1.1.1", "config": [{"config": {"dropout_U": 0.0, "name": "simplernn_1", "stateful": true, "input_dtype": "float32", "unroll": false, "U_regularizer": null, "init": "uniform", "inner_init": "uniform", "dropout_W": 0.0, "output_dim": 50, "batch_input_shape": [1, 1, 79], "return_sequences": false, "trainable": true, "W_regularizer": null, "go_backwards": false, "b_regularizer": null, "consume_less": "cpu", "activation": "relu"}, "class_name": "SimpleRNN"}, {"config": {"name": "dense_1", "activity_regularizer": null, "trainable": true, "output_dim": 1, "b_constraint": null, "bias": true, "activation": "sigmoid", "input_dim": null, "init": "uniform", "W_regularizer": null, "W_constraint": null, "b_regularizer": null}, "class_name": "Dense"}], "class_name": "Sequential"}
\ No newline at end of file
diff --git a/RNNmodels/RNN_1A.png b/RNNmodels/RNN_1A.png
new file mode 100644
index 0000000..9f62edb
Binary files /dev/null and b/RNNmodels/RNN_1A.png differ
diff --git a/RNNmodels/RNN_1B.json b/RNNmodels/RNN_1B.json
new file mode 100644
index 0000000..5df4f11
--- /dev/null
+++ b/RNNmodels/RNN_1B.json
@@ -0,0 +1 @@
+{"class_name": "Sequential", "keras_version": "1.1.1", "config": [{"class_name": "SimpleRNN", "config": {"consume_less": "cpu", "inner_init": "uniform", "return_sequences": false, "output_dim": 50, "W_regularizer": null, "go_backwards": false, "batch_input_shape": [1, 1, 79], "trainable": true, "b_regularizer": null, "stateful": true, "unroll": false, "U_regularizer": null, "activation": "relu", "input_dtype": "float32", "name": "simplernn_1", "dropout_U": 0.0, "dropout_W": 0.0, "init": "uniform"}}, {"class_name": "Dense", "config": {"activity_regularizer": null, "output_dim": 3, "W_regularizer": null, "W_constraint": null, "b_constraint": null, "activation": "softmax", "trainable": true, "b_regularizer": null, "bias": true, "input_dim": null, "name": "dense_1", "init": "uniform"}}]}
\ No newline at end of file
diff --git a/RNNmodels/RNN_1B.png b/RNNmodels/RNN_1B.png
new file mode 100644
index 0000000..9f62edb
Binary files /dev/null and b/RNNmodels/RNN_1B.png differ
diff --git a/RNNmodels/RNN_1C.json b/RNNmodels/RNN_1C.json
new file mode 100644
index 0000000..62c13c6
--- /dev/null
+++ b/RNNmodels/RNN_1C.json
@@ -0,0 +1 @@
+{"keras_version": "1.1.1", "class_name": "Sequential", "config": [{"class_name": "SimpleRNN", "config": {"trainable": true, "go_backwards": false, "input_dtype": "float32", "activation": "relu", "unroll": false, "U_regularizer": null, "init": "uniform", "b_regularizer": null, "batch_input_shape": [1, 1, 79], "name": "simplernn_1", "consume_less": "cpu", "inner_init": "uniform", "stateful": true, "output_dim": 50, "dropout_U": 0.0, "return_sequences": false, "dropout_W": 0.0, "W_regularizer": null}}, {"class_name": "Dense", "config": {"trainable": true, "input_dim": null, "activation": "softmax", "W_constraint": null, "activity_regularizer": null, "b_constraint": null, "init": "uniform", "b_regularizer": null, "name": "dense_1", "output_dim": 5, "bias": true, "W_regularizer": null}}]}
\ No newline at end of file
diff --git a/RNNmodels/RNN_1C.png b/RNNmodels/RNN_1C.png
new file mode 100644
index 0000000..9f62edb
Binary files /dev/null and b/RNNmodels/RNN_1C.png differ
diff --git a/RNNresults/Dataset Dataset0, Model RNN_1C.png b/RNNresults/Dataset Dataset0, Model RNN_1C.png
new file mode 100644
index 0000000..48d54c7
Binary files /dev/null and b/RNNresults/Dataset Dataset0, Model RNN_1C.png differ
diff --git a/RNNresults/Dataset Dataset1, RNN RNN_1A.png b/RNNresults/Dataset Dataset1, RNN RNN_1A.png
new file mode 100644
index 0000000..a727bdc
Binary files /dev/null and b/RNNresults/Dataset Dataset1, RNN RNN_1A.png differ
diff --git a/RNNresults/Dataset Dataset2, RNN RNN_1A.png b/RNNresults/Dataset Dataset2, RNN RNN_1A.png
new file mode 100644
index 0000000..cbb216b
Binary files /dev/null and b/RNNresults/Dataset Dataset2, RNN RNN_1A.png differ
diff --git a/RNNresults/Dataset Dataset3, RNN RNN_1A.png b/RNNresults/Dataset Dataset3, RNN RNN_1A.png
new file mode 100644
index 0000000..3821e84
Binary files /dev/null and b/RNNresults/Dataset Dataset3, RNN RNN_1A.png differ
diff --git a/RNNresults/Dataset: Dataset5, Model RNN_1B.png b/RNNresults/Dataset: Dataset5, Model RNN_1B.png
new file mode 100644
index 0000000..e369209
Binary files /dev/null and b/RNNresults/Dataset: Dataset5, Model RNN_1B.png differ
diff --git a/RNNresults/OLD/RNNresultsDataset2 b/RNNresults/OLD/RNNresultsDataset2
new file mode 100644
index 0000000..4537eb2
--- /dev/null
+++ b/RNNresults/OLD/RNNresultsDataset2
@@ -0,0 +1,45 @@
+RNN:: Loss Function:binary_crossentropy,   Optimizer:adagrad,   Batch Size:1,   Dropout Rate:No
+Loss Function: binary_crossentropy   Epoch: 0   Prec: 0.995253584705   Rec: 0.9995   F: 0.997372272485
+Loss Function: binary_crossentropy   Epoch: 1   Prec: 0.995452181514   Rec: 0.999577777778   F: 0.99751071391
+Loss Function: binary_crossentropy   Epoch: 2   Prec: 0.9956066089   Rec: 0.999622222222   F: 0.997610374633
+Loss Function: binary_crossentropy   Epoch: 3   Prec: 0.995760976636   Rec: 0.999644444444   F: 0.997698931528
+Loss Function: binary_crossentropy   Epoch: 4   Prec: 0.995838175881   Rec: 0.999655555556   F: 0.997743214395
+Loss Function: binary_crossentropy   Epoch: 5   Prec: 0.995926454798   Rec: 0.999677777778   F: 0.997798590432
+Loss Function: binary_crossentropy   Epoch: 6   Prec: 0.99594877301   Rec: 0.999744444444   F: 0.997842999174
+Loss Function: binary_crossentropy   Epoch: 7   Prec: 0.99601487779   Rec: 0.999733333333   F: 0.997870641469
+Loss Function: binary_crossentropy   Epoch: 8   Prec: 0.9960700962   Rec: 0.999755555556   F: 0.997909423123
+Loss Function: binary_crossentropy   Epoch: 9   Prec: 0.99611420474   Rec: 0.999755555556   F: 0.997931558429
+Loss Function: binary_crossentropy   Epoch: 10   Prec: 0.996136388797   Rec: 0.999788888889   F: 0.997959296845
+Loss Function: binary_crossentropy   Epoch: 11   Prec: 0.996191530584   Rec: 0.999788888889   F: 0.997986967974
+Loss Function: binary_crossentropy   Epoch: 12   Prec: 0.99623564841   Rec: 0.999788888889   F: 0.998009105983
+Loss Function: binary_crossentropy   Epoch: 13   Prec: 0.996323936488   Rec: 0.9998   F: 0.998058941624
+Loss Function: binary_crossentropy   Epoch: 14   Prec: 0.996357073257   Rec: 0.999811111111   F: 0.998081103864
+Loss Function: binary_crossentropy   Epoch: 15   Prec: 0.996379058334   Rec: 0.999788888889   F: 0.998081061295
+Loss Function: binary_crossentropy   Epoch: 16   Prec: 0.996434226991   Rec: 0.999788888889   F: 0.998108739178
+Loss Function: binary_crossentropy   Epoch: 17   Prec: 0.996467331118   Rec: 0.999788888889   F: 0.998125346644
+Loss Function: binary_crossentropy   Epoch: 18   Prec: 0.996478288315   Rec: 0.999766666667   F: 0.998119769048
+Loss Function: binary_crossentropy   Epoch: 19   Prec: 0.996522432525   Rec: 0.999766666667   F: 0.998141913441
+Loss Function: binary_crossentropy   Epoch: 20   Prec: 0.99655554325   Rec: 0.999766666667   F: 0.998158522381
+RNN:: Loss Function:mse,   Optimizer:adagrad,   Batch Size:1,   Dropout Rate:No
+Loss Function: mse   Epoch: 0   Prec: 0.996677004874   Rec: 0.999777777778   F: 0.998224983359
+Loss Function: mse   Epoch: 1   Prec: 0.996732098547   Rec: 0.999744444444   F: 0.998235998935
+Loss Function: mse   Epoch: 2   Prec: 0.996765188107   Rec: 0.999733333333   F: 0.998247054386
+Loss Function: mse   Epoch: 3   Prec: 0.996798245128   Rec: 0.999711111111   F: 0.998252553214
+Loss Function: mse   Epoch: 4   Prec: 0.996809182464   Rec: 0.999677777778   F: 0.998241419291
+Loss Function: mse   Epoch: 5   Prec: 0.996809111759   Rec: 0.999655555556   F: 0.99823030451
+Loss Function: mse   Epoch: 6   Prec: 0.996819979834   Rec: 0.9996   F: 0.998208054324
+Loss Function: mse   Epoch: 7   Prec: 0.996842000288   Rec: 0.999577777778   F: 0.998208014558
+Loss Function: mse   Epoch: 8   Prec: 0.996864057444   Rec: 0.999566666667   F: 0.998213532767
+Loss Function: mse   Epoch: 9   Prec: 0.996886047053   Rec: 0.999533333333   F: 0.99820793502
+Loss Function: mse   Epoch: 10   Prec: 0.99689699119   Rec: 0.9995   F: 0.998196798624
+Loss Function: mse   Epoch: 11   Prec: 0.996885909015   Rec: 0.999488888889   F: 0.998185702
+Loss Function: mse   Epoch: 12   Prec: 0.996918916523   Rec: 0.999444444444   F: 0.998180083006
+Loss Function: mse   Epoch: 13   Prec: 0.996940777442   Rec: 0.999366666667   F: 0.998152248098
+Loss Function: mse   Epoch: 14   Prec: 0.9969406418   Rec: 0.999322222222   F: 0.998130011375
+Loss Function: mse   Epoch: 15   Prec: 0.996940540061   Rec: 0.999288888889   F: 0.998113333185
+Loss Function: mse   Epoch: 16   Prec: 0.996929318907   Rec: 0.999233333333   F: 0.998079996449
+Loss Function: mse   Epoch: 17   Prec: 0.996940336563   Rec: 0.999222222222   F: 0.99807997514
+Loss Function: mse   Epoch: 18   Prec: 0.996929216784   Rec: 0.9992   F: 0.998063316778
+Loss Function: mse   Epoch: 19   Prec: 0.996918062592   Rec: 0.999166666667   F: 0.998041098095
+Loss Function: mse   Epoch: 20   Prec: 0.996929046563   Rec: 0.999144444444   F: 0.998035516093
+
diff --git a/RNNresults/OLD/RNNresultsDataset4 b/RNNresults/OLD/RNNresultsDataset4
new file mode 100644
index 0000000..e69de29
diff --git a/RNNresults/OLD/RNNresultsDataset5 b/RNNresults/OLD/RNNresultsDataset5
new file mode 100644
index 0000000..cfb539c
--- /dev/null
+++ b/RNNresults/OLD/RNNresultsDataset5
@@ -0,0 +1,45 @@
+RNN Dataset 5   Loss Function:categorical_crossentropy,   Optimizer:sgd,   Batch Size:1,   Dropout Rate:No
+Loss Function: categorical_crossentropy   Epoch: 0   Prec: 0.954327956989
+Loss Function: categorical_crossentropy   Epoch: 1   Prec: 0.951948924731
+Loss Function: categorical_crossentropy   Epoch: 2   Prec: 0.94686827957
+Loss Function: categorical_crossentropy   Epoch: 3   Prec: 0.962668010753
+Loss Function: categorical_crossentropy   Epoch: 4   Prec: 0.894670698925
+Loss Function: categorical_crossentropy   Epoch: 5   Prec: 0.945329301075
+Loss Function: categorical_crossentropy   Epoch: 6   Prec: 0.872909946237
+Loss Function: categorical_crossentropy   Epoch: 7   Prec: 0.919220430108
+Loss Function: categorical_crossentropy   Epoch: 8   Prec: 0.913313172043
+Loss Function: categorical_crossentropy   Epoch: 9   Prec: 0.871142473118
+Loss Function: categorical_crossentropy   Epoch: 10   Prec: 0.905241935484
+Loss Function: categorical_crossentropy   Epoch: 11   Prec: 0.96063844086
+Loss Function: categorical_crossentropy   Epoch: 12   Prec: 0.932043010753
+Loss Function: categorical_crossentropy   Epoch: 13   Prec: 0.915887096774
+Loss Function: categorical_crossentropy   Epoch: 14   Prec: 0.911041666667
+Loss Function: categorical_crossentropy   Epoch: 15   Prec: 0.883877688172
+Loss Function: categorical_crossentropy   Epoch: 16   Prec: 0.910766129032
+Loss Function: categorical_crossentropy   Epoch: 17   Prec: 0.90061155914
+Loss Function: categorical_crossentropy   Epoch: 18   Prec: 0.931424731183
+Loss Function: categorical_crossentropy   Epoch: 19   Prec: 0.957392473118
+Loss Function: categorical_crossentropy   Epoch: 20   Prec: 0.820053763441
+RNN Dataset 5   Loss Function:mse,   Optimizer:sgd,   Batch Size:1,   Dropout Rate:No
+Loss Function: mse   Epoch: 0   Prec: 0.965194892473
+Loss Function: mse   Epoch: 1   Prec: 0.965712365591
+Loss Function: mse   Epoch: 2   Prec: 0.957889784946
+Loss Function: mse   Epoch: 3   Prec: 0.944932795699
+Loss Function: mse   Epoch: 4   Prec: 0.936532258065
+Loss Function: mse   Epoch: 5   Prec: 0.923501344086
+Loss Function: mse   Epoch: 6   Prec: 0.921955645161
+Loss Function: mse   Epoch: 7   Prec: 0.925181451613
+Loss Function: mse   Epoch: 8   Prec: 0.930739247312
+Loss Function: mse   Epoch: 9   Prec: 0.93624327957
+Loss Function: mse   Epoch: 10   Prec: 0.917318548387
+Loss Function: mse   Epoch: 11   Prec: 0.930080645161
+Loss Function: mse   Epoch: 12   Prec: 0.94377016129
+Loss Function: mse   Epoch: 13   Prec: 0.947607526882
+Loss Function: mse   Epoch: 14   Prec: 0.947130376344
+Loss Function: mse   Epoch: 15   Prec: 0.948965053763
+Loss Function: mse   Epoch: 16   Prec: 0.949106182796
+Loss Function: mse   Epoch: 17   Prec: 0.948427419355
+Loss Function: mse   Epoch: 18   Prec: 0.957634408602
+Loss Function: mse   Epoch: 19   Prec: 0.949415322581
+Loss Function: mse   Epoch: 20   Prec: 0.963420698925
+
diff --git a/RNNresults/OLD/RNNresultsDataset6 b/RNNresults/OLD/RNNresultsDataset6
new file mode 100644
index 0000000..9279cb0
--- /dev/null
+++ b/RNNresults/OLD/RNNresultsDataset6
@@ -0,0 +1,5 @@
+Dataset6 RNN:: Loss Function: mse,  Optimizer: adagrad,  Batch Size:1,  Dropout Rate:No
+Loss Function: mse   Epoch:    Prec: 0.988579921967   Rec: 0.995633895937   F: 0.992094370346
+Loss Function: mse   Epoch: 1   Prec: 0.988579921967   Rec: 0.995633895937   F: 0.992094370346
+Loss Function: mse   Epoch: 2   Prec: 0.989636663402   Rec: 0.995883387598   F: 0.992750198965
+Loss Function: mse   Epoch: 2   Prec: 0.987699513157   Rec: 0.999675660841   F: 0.993651502188
diff --git a/RNNresults/OLD/Z.RNN Dataset 2 Losses: Binary-Crossentropy, MSE  Opt: adagrad.png b/RNNresults/OLD/Z.RNN Dataset 2 Losses: Binary-Crossentropy, MSE  Opt: adagrad.png
new file mode 100644
index 0000000..584773a
Binary files /dev/null and b/RNNresults/OLD/Z.RNN Dataset 2 Losses: Binary-Crossentropy, MSE  Opt: adagrad.png differ
diff --git a/RNNresults/OLD/Z.RNN Dataset 5 Losses: Crossentropy, MSE  Opt: sgd.png b/RNNresults/OLD/Z.RNN Dataset 5 Losses: Crossentropy, MSE  Opt: sgd.png
new file mode 100644
index 0000000..1cdd290
Binary files /dev/null and b/RNNresults/OLD/Z.RNN Dataset 5 Losses: Crossentropy, MSE  Opt: sgd.png differ
diff --git a/RNNresults/OLD/Z.RNN Dataset 6 Loss: MSE  Opt: adagrad.png b/RNNresults/OLD/Z.RNN Dataset 6 Loss: MSE  Opt: adagrad.png
new file mode 100644
index 0000000..e66445d
Binary files /dev/null and b/RNNresults/OLD/Z.RNN Dataset 6 Loss: MSE  Opt: adagrad.png differ
diff --git a/RNNresults/RNN_1A.Dataset1 b/RNNresults/RNN_1A.Dataset1
new file mode 100644
index 0000000..a0de725
--- /dev/null
+++ b/RNNresults/RNN_1A.Dataset1
@@ -0,0 +1,217 @@
+Model:RNN_1A Dataset:Dataset1 Loss:mae Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.27217541182|0.928|0.874125874126|1.0|0.932835820896
+2|0.0419483531476|0.997|0.994035785288|1.0|0.997008973081
+3|0.0192993114126|1.0|1.0|1.0|1.0
+4|0.0120467033392|1.0|1.0|1.0|1.0
+5|0.00852892059516|0.999|0.998003992016|1.0|0.999000999001
+6|0.00663071068552|0.998|0.996015936255|1.0|0.998003992016
+Model:RNN_1A Dataset:Dataset1 Loss:mae Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.500874960481|0.5|nan|0.0|nan
+Model:RNN_1A Dataset:Dataset1 Loss:mae Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+Model:RNN_1A Dataset:Dataset1 Loss:mae Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+Model:RNN_1A Dataset:Dataset1 Loss:mae Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+Model:RNN_1A Dataset:Dataset1 Loss:mae Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+Model:RNN_1A Dataset:Dataset1 Loss:mae Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+Model:RNN_1A Dataset:Dataset1 Loss:mae Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.226321996421|0.987|0.974658869396|1.0|0.987166831194
+2|0.0396751123704|0.997|0.994035785288|1.0|0.997008973081
+3|0.0199277004448|1.0|1.0|1.0|1.0
+4|0.0125076925369|1.0|1.0|1.0|1.0
+5|0.00878011060897|0.998|0.996015936255|1.0|0.998003992016
+6|0.0067885634983|0.998|0.996015936255|1.0|0.998003992016
+Model:RNN_1A Dataset:Dataset1 Loss:mae Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0212864208655|1.0|1.0|1.0|1.0
+2|0.0125278775205|1.0|1.0|1.0|1.0
+3|0.00857428762691|1.0|1.0|1.0|1.0
+4|0.00639353147987|1.0|1.0|1.0|1.0
+5|0.0050610439586|1.0|1.0|1.0|1.0
+6|0.0041947777895|1.0|1.0|1.0|1.0
+Model:RNN_1A Dataset:Dataset1 Loss:mae Optimizer:rmsprop Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0146710907442|0.997|0.994035785288|1.0|0.997008973081
+2|0.0032516931921|0.999|0.998003992016|1.0|0.999000999001
+3|0.00206779985995|0.999|0.998003992016|1.0|0.999000999001
+4|0.00183539554915|0.998|0.998|0.998|0.998
+5|0.00242123537257|0.997|0.994035785288|1.0|0.997008973081
+6|0.0022028519602|0.998|0.996015936255|1.0|0.998003992016
+Model:RNN_1A Dataset:Dataset1 Loss:mse Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.11282326106|0.977|0.956022944551|1.0|0.977517106549
+2|0.0183457814367|0.997|0.994035785288|1.0|0.997008973081
+3|0.00926305155794|1.0|1.0|1.0|1.0
+4|0.00580813262537|0.999|0.998003992016|1.0|0.999000999001
+5|0.00418871423786|0.998|0.996015936255|1.0|0.998003992016
+6|0.00333216788031|0.998|0.996015936255|1.0|0.998003992016
+Model:RNN_1A Dataset:Dataset1 Loss:mse Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.00555417128769|1.0|1.0|1.0|1.0
+2|0.00302195425023|1.0|1.0|1.0|1.0
+3|0.00204649192547|1.0|1.0|1.0|1.0
+4|0.00155100670725|0.999|0.998003992016|1.0|0.999000999001
+5|0.00126465564738|0.999|0.998003992016|1.0|0.999000999001
+6|0.00108725838747|0.999|0.998003992016|1.0|0.999000999001
+Model:RNN_1A Dataset:Dataset1 Loss:mse Optimizer:rmsprop Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.00317029752331|0.999|0.998003992016|1.0|0.999000999001
+2|0.000738143724928|0.999|0.998003992016|1.0|0.999000999001
+3|0.000740886563754|0.999|0.998003992016|1.0|0.999000999001
+4|0.000848890815514|0.999|0.998003992016|1.0|0.999000999001
+5|0.00126848833919|0.999|0.998003992016|1.0|0.999000999001
+6|0.00202674920985|0.997|0.994035785288|1.0|0.997008973081
+Model:RNN_1A Dataset:Dataset1 Loss:categorical_crossentropy Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+Model:RNN_1A Dataset:Dataset1 Loss:mae Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+Model:RNN_1A Dataset:Dataset1 Loss:mae Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.227167929962|0.962|0.92936802974|1.0|0.963391136802
+2|0.0391965886557|0.997|0.994035785288|1.0|0.997008973081
+3|0.0192875620797|1.0|1.0|1.0|1.0
+4|0.0120902967854|1.0|1.0|1.0|1.0
+5|0.00852546518815|0.999|0.998003992016|1.0|0.999000999001
+6|0.00662239622843|0.998|0.996015936255|1.0|0.998003992016
+Model:RNN_1A Dataset:Dataset1 Loss:mae Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0183688909953|1.0|1.0|1.0|1.0
+2|0.0101230356989|1.0|1.0|1.0|1.0
+3|0.00714074342779|1.0|1.0|1.0|1.0
+4|0.00540353864088|1.0|1.0|1.0|1.0
+5|0.00432211198631|1.0|1.0|1.0|1.0
+6|0.00360471337203|0.999|0.998003992016|1.0|0.999000999001
+Model:RNN_1A Dataset:Dataset1 Loss:mae Optimizer:rmsprop Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0131138948497|0.997|0.994035785288|1.0|0.997008973081
+2|0.0022528526906|0.999|0.998003992016|1.0|0.999000999001
+3|0.00113505070793|0.999|0.998003992016|1.0|0.999000999001
+4|0.00120193922905|0.999|0.998003992016|1.0|0.999000999001
+5|0.00104698447558|0.999|0.998003992016|1.0|0.999000999001
+6|0.00118081037481|0.999|0.998003992016|1.0|0.999000999001
+Model:RNN_1A Dataset:Dataset1 Loss:mse Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.110679095142|0.986|0.972762645914|1.0|0.986193293886
+2|0.017694292252|0.997|0.994035785288|1.0|0.997008973081
+3|0.00884300011082|0.999|0.998003992016|1.0|0.999000999001
+4|0.00565075502825|0.998|0.996015936255|1.0|0.998003992016
+5|0.0041702277416|0.998|0.996015936255|1.0|0.998003992016
+6|0.00338408249904|0.998|0.996015936255|1.0|0.998003992016
+Model:RNN_1A Dataset:Dataset1 Loss:mse Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.00582260105601|1.0|1.0|1.0|1.0
+2|0.00321129900365|1.0|1.0|1.0|1.0
+3|0.00213683597205|1.0|1.0|1.0|1.0
+4|0.00158551350906|1.0|1.0|1.0|1.0
+5|0.00126606668131|1.0|1.0|1.0|1.0
+6|0.0010675778917|1.0|1.0|1.0|1.0
+Model:RNN_1A Dataset:Dataset1 Loss:mse Optimizer:rmsprop Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.00310717387467|0.999|0.998003992016|1.0|0.999000999001
+2|0.000666832010265|0.999|0.998003992016|1.0|0.999000999001
+3|0.00109820591866|0.999|0.998003992016|1.0|0.999000999001
+4|0.000609260196693|0.999|0.998003992016|1.0|0.999000999001
+5|0.000711018829898|0.999|0.998003992016|1.0|0.999000999001
+6|0.00114193155961|0.999|0.998003992016|1.0|0.999000999001
+Model:RNN_1A Dataset:Dataset1 Loss:binary_crossentropy Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.066592183915|0.997|0.994035785288|1.0|0.997008973081
+2|0.0234933371256|1.0|1.0|1.0|1.0
+3|0.0134420820984|1.0|1.0|1.0|1.0
+4|0.0093750679581|0.998|0.996015936255|1.0|0.998003992016
+5|0.00748813748587|0.998|0.996015936255|1.0|0.998003992016
+6|0.0065276356508|0.998|0.996015936255|1.0|0.998003992016
+Model:RNN_1A Dataset:Dataset1 Loss:binary_crossentropy Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0415063531269|0.998|0.996015936255|1.0|0.998003992016
+2|0.0214204211729|1.0|1.0|1.0|1.0
+3|0.0134652488659|1.0|1.0|1.0|1.0
+4|0.00954230818904|0.999|0.998003992016|1.0|0.999000999001
+5|0.00783514435727|0.999|0.998003992016|1.0|0.999000999001
+6|0.00660118782402|0.999|0.998003992016|1.0|0.999000999001
+Model:RNN_1A Dataset:Dataset1 Loss:binary_crossentropy Optimizer:rmsprop Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0162422141817|1.0|1.0|1.0|1.0
+2|0.00307897194079|0.999|0.998003992016|1.0|0.999000999001
+3|0.00372211121709|0.999|0.998003992016|1.0|0.999000999001
+4|0.00421627859261|0.999|0.998003992016|1.0|0.999000999001
+5|0.0136286041544|0.996|0.992063492063|1.0|0.996015936255
+6|0.00333451359286|0.999|0.998003992016|1.0|0.999000999001
+Model:RNN_1A Dataset:Dataset1 Loss:mae Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.189609860152|0.956|0.919117647059|1.0|0.95785440613
+2|0.0384124780361|0.997|0.994035785288|1.0|0.997008973081
+3|0.0197211649245|1.0|1.0|1.0|1.0
+4|0.0125759543237|1.0|1.0|1.0|1.0
+5|0.00892382730719|0.998|0.996015936255|1.0|0.998003992016
+6|0.00693168768667|0.998|0.996015936255|1.0|0.998003992016
+Model:RNN_1A Dataset:Dataset1 Loss:mae Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0181725211229|1.0|1.0|1.0|1.0
+2|0.0105247569788|1.0|1.0|1.0|1.0
+3|0.00720328102259|1.0|1.0|1.0|1.0
+4|0.00540503132327|1.0|1.0|1.0|1.0
+5|0.00429908872564|1.0|1.0|1.0|1.0
+6|0.00356712109341|1.0|1.0|1.0|1.0
+Model:RNN_1A Dataset:Dataset1 Loss:mae Optimizer:rmsprop Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0117345524773|0.995|0.990099009901|1.0|0.995024875622
+2|0.00296888438304|0.999|0.998003992016|1.0|0.999000999001
+3|0.00143563692473|0.999|0.998003992016|1.0|0.999000999001
+4|0.00144988820385|0.999|0.998003992016|1.0|0.999000999001
+5|0.00196386442172|0.998|0.996015936255|1.0|0.998003992016
+6|0.00208874012206|0.998|0.996015936255|1.0|0.998003992016
+Model:RNN_1A Dataset:Dataset1 Loss:mse Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0888121894076|0.982|0.965250965251|1.0|0.98231827112
+2|0.0179555182909|0.997|0.994035785288|1.0|0.997008973081
+3|0.00904757790721|0.999|0.998003992016|1.0|0.999000999001
+4|0.00578965376667|0.998|0.996015936255|1.0|0.998003992016
+5|0.00428028540192|0.998|0.996015936255|1.0|0.998003992016
+6|0.00347508132312|0.997|0.994035785288|1.0|0.997008973081
+Model:RNN_1A Dataset:Dataset1 Loss:mse Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.00640067315864|1.0|1.0|1.0|1.0
+2|0.00340981556942|1.0|1.0|1.0|1.0
+3|0.00233331428543|1.0|1.0|1.0|1.0
+4|0.00174848726792|1.0|1.0|1.0|1.0
+5|0.00140064503817|0.999|0.998003992016|1.0|0.999000999001
+6|0.00118423646728|0.999|0.998003992016|1.0|0.999000999001
+Model:RNN_1A Dataset:Dataset1 Loss:mse Optimizer:rmsprop Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.00429901678899|1.0|1.0|1.0|1.0
+2|0.000831232460678|0.999|0.998003992016|1.0|0.999000999001
+3|0.000818787679001|0.999|0.998003992016|1.0|0.999000999001
+4|0.00112579185018|0.999|0.998003992016|1.0|0.999000999001
+5|0.00118049989595|0.999|0.998003992016|1.0|0.999000999001
+6|0.0011277411167|0.999|0.998003992016|1.0|0.999000999001
+Model:RNN_1A Dataset:Dataset1 Loss:binary_crossentropy Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0700609523277|0.997|0.994035785288|1.0|0.997008973081
+2|0.0237764141481|1.0|1.0|1.0|1.0
+3|0.013446193388|1.0|1.0|1.0|1.0
+4|0.00929553388188|0.998|0.996015936255|1.0|0.998003992016
+5|0.00738171943929|0.998|0.996015936255|1.0|0.998003992016
+6|0.00641020268091|0.998|0.996015936255|1.0|0.998003992016
+Model:RNN_1A Dataset:Dataset1 Loss:binary_crossentropy Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.037881562763|1.0|1.0|1.0|1.0
+2|0.0202241999793|1.0|1.0|1.0|1.0
+3|0.0145777964831|1.0|1.0|1.0|1.0
+4|0.00933628657007|1.0|1.0|1.0|1.0
+5|0.00755264579274|1.0|1.0|1.0|1.0
+6|0.0063438233579|0.999|0.998003992016|1.0|0.999000999001
+Model:RNN_1A Dataset:Dataset1 Loss:binary_crossentropy Optimizer:rmsprop Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0178695219376|1.0|1.0|1.0|1.0
+2|0.00174207979748|0.999|0.998003992016|1.0|0.999000999001
+3|0.00104147204825|0.999|0.998003992016|1.0|0.999000999001
+4|0.00287536002184|0.999|0.998003992016|1.0|0.999000999001
+5|0.000767268353506|0.999|0.998003992016|1.0|0.999000999001
+6|0.0013445660986|0.999|0.998003992016|1.0|0.999000999001
diff --git a/RNNresults/RNN_1A.Dataset2 b/RNNresults/RNN_1A.Dataset2
new file mode 100644
index 0000000..cbec0b4
--- /dev/null
+++ b/RNNresults/RNN_1A.Dataset2
@@ -0,0 +1,144 @@
+Model:RNN_1A Dataset:Dataset2 Loss:mae Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.377747594446|0.773|0.687757909216|1.0|0.81499592502
+2|0.106800073247|0.927|0.896103896104|0.966|0.929740134745
+3|0.0619023300644|0.968|0.93984962406|1.0|0.968992248062
+4|0.0450360807332|0.974|0.950570342205|1.0|0.974658869396
+5|0.03519027031|0.979|0.959692898273|1.0|0.979431929481
+6|0.0289734979511|0.98|0.961538461538|1.0|0.980392156863
+Model:RNN_1A Dataset:Dataset2 Loss:mae Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0625287355388|0.976|0.954198473282|1.0|0.9765625
+2|0.0369267185817|0.976|0.954198473282|1.0|0.9765625
+3|0.0282753684097|0.982|0.965250965251|1.0|0.98231827112
+4|0.0237051222898|0.986|0.972762645914|1.0|0.986193293886
+5|0.0207900508571|0.987|0.974658869396|1.0|0.987166831194
+6|0.0187302961703|0.99|0.980392156863|1.0|0.990099009901
+Model:RNN_1A Dataset:Dataset2 Loss:mae Optimizer:rmsprop Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0650791488478|0.967|0.93808630394|1.0|0.968054211036
+2|0.0396882604253|0.958|0.922509225092|1.0|0.959692898273
+3|0.0301225594319|0.974|0.950570342205|1.0|0.974658869396
+4|0.0300425492405|0.969|0.941619585687|1.0|0.969932104753
+5|0.0294087932911|0.971|0.945179584121|1.0|0.971817298348
+6|0.0297330120692|0.972|0.94696969697|1.0|0.972762645914
+Model:RNN_1A Dataset:Dataset2 Loss:mse Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.191974764012|0.776|0.690607734807|1.0|0.816993464052
+2|0.0691287218444|0.938|0.889679715302|1.0|0.941619585687
+3|0.0359318422242|0.973|0.948766603416|1.0|0.973709834469
+4|0.0251890699168|0.975|0.952380952381|1.0|0.975609756098
+5|0.0196979209921|0.979|0.959692898273|1.0|0.979431929481
+6|0.0163959968814|0.98|0.961538461538|1.0|0.980392156863
+Model:RNN_1A Dataset:Dataset2 Loss:mse Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0262378091954|0.976|0.954198473282|1.0|0.9765625
+2|0.0160960768165|0.98|0.961538461538|1.0|0.980392156863
+3|0.0122247507541|0.987|0.974658869396|1.0|0.987166831194
+4|0.0101014009329|0.988|0.9765625|1.0|0.98814229249
+5|0.0087299845785|0.993|0.986193293886|1.0|0.993048659384
+6|0.00776110408369|0.995|0.990099009901|1.0|0.995024875622
+Model:RNN_1A Dataset:Dataset2 Loss:mse Optimizer:rmsprop Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0231606271591|0.969|0.941619585687|1.0|0.969932104753
+2|0.0150598505074|0.981|0.963391136802|1.0|0.981354268891
+3|0.0146300712453|0.976|0.954198473282|1.0|0.9765625
+4|0.0107698901831|0.981|0.963391136802|1.0|0.981354268891
+5|0.0050520988155|0.996|0.992063492063|1.0|0.996015936255
+6|0.00375492227608|0.998|0.996015936255|1.0|0.998003992016
+Model:RNN_1A Dataset:Dataset2 Loss:binary_crossentropy Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.199337044824|0.944|0.89928057554|1.0|0.94696969697
+2|0.0845595251565|0.974|0.950570342205|1.0|0.974658869396
+3|0.0541381883|0.981|0.963391136802|1.0|0.981354268891
+4|0.0410366149703|0.985|0.970873786408|1.0|0.985221674877
+5|0.0336838720829|0.986|0.972762645914|1.0|0.986193293886
+6|0.0288446413898|0.988|0.9765625|1.0|0.98814229249
+Model:RNN_1A Dataset:Dataset2 Loss:binary_crossentropy Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.104546684219|0.977|0.956022944551|1.0|0.977517106549
+2|0.0641593372675|0.983|0.967117988395|1.0|0.983284169125
+3|0.0474001060681|0.984|0.968992248062|1.0|0.984251968504
+4|0.038401312762|0.986|0.972762645914|1.0|0.986193293886
+5|0.0327547287168|0.987|0.974658869396|1.0|0.987166831194
+6|0.0288684219535|0.987|0.974658869396|1.0|0.987166831194
+Model:RNN_1A Dataset:Dataset2 Loss:binary_crossentropy Optimizer:rmsprop Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.110687843674|0.959|0.924214417745|1.0|0.960614793468
+2|0.066869454174|0.975|0.952380952381|1.0|0.975609756098
+3|0.0658065313685|0.973|0.948766603416|1.0|0.973709834469
+4|0.04877255463|0.978|0.95785440613|1.0|0.978473581213
+5|0.0298995051779|0.988|0.9765625|1.0|0.98814229249
+6|0.0197408971398|0.994|0.98814229249|1.0|0.994035785288
+Model:RNN_1A Dataset:Dataset2 Loss:mae Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.383146877885|0.699|0.624219725343|1.0|0.768639508071
+2|0.113656142011|0.926|0.891544117647|0.97|0.929118773946
+3|0.0639203492497|0.967|0.93808630394|1.0|0.968054211036
+4|0.0462107073822|0.971|0.945179584121|1.0|0.971817298348
+5|0.0359971404236|0.978|0.95785440613|1.0|0.978473581213
+6|0.0296148429333|0.98|0.961538461538|1.0|0.980392156863
+Model:RNN_1A Dataset:Dataset2 Loss:mae Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0686368738168|0.974|0.950570342205|1.0|0.974658869396
+2|0.0418647100022|0.977|0.956022944551|1.0|0.977517106549
+3|0.0320179507377|0.979|0.959692898273|1.0|0.979431929481
+4|0.0267228262588|0.984|0.968992248062|1.0|0.984251968504
+5|0.0233672731534|0.985|0.970873786408|1.0|0.985221674877
+6|0.0210324709242|0.988|0.9765625|1.0|0.98814229249
+Model:RNN_1A Dataset:Dataset2 Loss:mae Optimizer:rmsprop Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0673780469443|0.956|0.919117647059|1.0|0.95785440613
+2|0.045015385403|0.957|0.920810313076|1.0|0.958772770853
+3|0.0335312983909|0.964|0.932835820896|1.0|0.965250965251
+4|0.0323512320218|0.968|0.93984962406|1.0|0.968992248062
+5|0.0305677490983|0.971|0.945179584121|1.0|0.971817298348
+6|0.0284967916381|0.972|0.94696969697|1.0|0.972762645914
+Model:RNN_1A Dataset:Dataset2 Loss:mse Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.188898625106|0.866|0.788643533123|1.0|0.881834215168
+2|0.0601638681367|0.958|0.922509225092|1.0|0.959692898273
+3|0.0343229533256|0.973|0.948766603416|1.0|0.973709834469
+4|0.0243627264271|0.976|0.954198473282|1.0|0.9765625
+5|0.0190147449005|0.982|0.965250965251|1.0|0.98231827112
+6|0.0157507972663|0.985|0.970873786408|1.0|0.985221674877
+Model:RNN_1A Dataset:Dataset2 Loss:mse Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0233828094922|0.978|0.95785440613|1.0|0.978473581213
+2|0.0140391264066|0.982|0.965250965251|1.0|0.98231827112
+3|0.0105963866321|0.987|0.974658869396|1.0|0.987166831194
+4|0.00875526780694|0.995|0.990099009901|1.0|0.995024875622
+5|0.00759526559382|0.995|0.990099009901|1.0|0.995024875622
+6|0.00678498406197|0.996|0.992063492063|1.0|0.996015936255
+Model:RNN_1A Dataset:Dataset2 Loss:mse Optimizer:rmsprop Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0225317217468|0.972|0.94696969697|1.0|0.972762645914
+2|0.0154641615403|0.976|0.954198473282|1.0|0.9765625
+3|0.0152435779126|0.976|0.954198473282|1.0|0.9765625
+4|0.00967729578245|0.985|0.970873786408|1.0|0.985221674877
+5|0.00636785466466|0.994|0.98814229249|1.0|0.994035785288
+6|0.00649192125729|0.994|0.98814229249|1.0|0.994035785288
+Model:RNN_1A Dataset:Dataset2 Loss:binary_crossentropy Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.219525096755|0.932|0.880281690141|1.0|0.936329588015
+2|0.0852461810242|0.976|0.954198473282|1.0|0.9765625
+3|0.0541084285156|0.982|0.965250965251|1.0|0.98231827112
+4|0.0409071020193|0.985|0.970873786408|1.0|0.985221674877
+5|0.0334927361979|0.987|0.974658869396|1.0|0.987166831194
+6|0.0286289837653|0.988|0.9765625|1.0|0.98814229249
+Model:RNN_1A Dataset:Dataset2 Loss:binary_crossentropy Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.115072413822|0.974|0.950570342205|1.0|0.974658869396
+2|0.0737210123093|0.977|0.956022944551|1.0|0.977517106549
+3|0.056702816851|0.978|0.95785440613|1.0|0.978473581213
+4|0.0469326916503|0.98|0.961538461538|1.0|0.980392156863
+5|0.0405558966795|0.985|0.970873786408|1.0|0.985221674877
+6|0.0359795311008|0.987|0.974658869396|1.0|0.987166831194
+Model:RNN_1A Dataset:Dataset2 Loss:binary_crossentropy Optimizer:rmsprop Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.104056815082|0.961|0.927643784787|1.0|0.962463907603
+2|0.0860237835099|0.959|0.924214417745|1.0|0.960614793468
+3|0.0634022794076|0.97|0.943396226415|1.0|0.970873786408
+4|0.0626246238794|0.971|0.945179584121|1.0|0.971817298348
+5|0.0504154382271|0.975|0.952380952381|1.0|0.975609756098
+6|0.0295439553615|0.99|0.980392156863|1.0|0.990099009901
diff --git a/RNNresults/RNN_1A.Dataset3 b/RNNresults/RNN_1A.Dataset3
new file mode 100644
index 0000000..6d2044b
--- /dev/null
+++ b/RNNresults/RNN_1A.Dataset3
@@ -0,0 +1,144 @@
+Model:RNN_1A Dataset:Dataset3 Loss:mae Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.50058988592|0.496|0.0|0.0|nan
+2|0.506296645385|0.5|0.0|0.0|nan
+3|0.507883972828|0.497|0.0|0.0|nan
+4|0.50877714545|0.496|0.0|0.0|nan
+5|0.507811402978|0.496|0.0|0.0|nan
+6|0.506921213188|0.496|0.0|0.0|nan
+Model:RNN_1A Dataset:Dataset3 Loss:mae Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.511891267207|0.497|0.2|0.002|0.0039603960396
+2|0.510136637388|0.496|0.0|0.0|nan
+3|0.508480044661|0.496|0.0|0.0|nan
+4|0.507393837287|0.496|0.0|0.0|nan
+5|0.506766362811|0.496|0.0|0.0|nan
+6|0.506367664599|0.496|0.0|0.0|nan
+Model:RNN_1A Dataset:Dataset3 Loss:mae Optimizer:rmsprop Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.524798675289|0.495|0.142857142857|0.002|0.00394477317554
+2|0.506134653951|0.496|0.0|0.0|nan
+3|0.503994754391|0.496|0.0|0.0|nan
+4|0.503904881829|0.496|0.0|0.0|nan
+5|0.503991544856|0.496|0.0|0.0|nan
+6|0.503993995277|0.496|0.0|0.0|nan
+Model:RNN_1A Dataset:Dataset3 Loss:mse Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.252988466322|0.494|0.0|0.0|nan
+2|0.327640216038|0.496|0.0|0.0|nan
+3|0.430162218974|0.497|0.0|0.0|nan
+4|0.446708267141|0.496|0.0|0.0|nan
+5|0.457335546589|0.496|0.0|0.0|nan
+6|0.464767147289|0.496|0.0|0.0|nan
+Model:RNN_1A Dataset:Dataset3 Loss:mse Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.426876651131|0.496|0.0|0.0|nan
+2|0.448305015794|0.496|0.0|0.0|nan
+3|0.458941169391|0.496|0.0|0.0|nan
+4|0.465335760254|0.496|0.0|0.0|nan
+5|0.469822874723|0.496|0.0|0.0|nan
+6|0.473269270881|0.496|0.0|0.0|nan
+Model:RNN_1A Dataset:Dataset3 Loss:mse Optimizer:rmsprop Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.437171928487|0.496|0.0|0.0|nan
+2|0.493084128891|0.496|0.0|0.0|nan
+3|0.503487639617|0.496|0.0|0.0|nan
+4|0.503961811118|0.496|0.0|0.0|nan
+5|0.503988810218|0.496|0.0|0.0|nan
+6|0.503993721116|0.496|0.0|0.0|nan
+Model:RNN_1A Dataset:Dataset3 Loss:binary_crossentropy Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.91130969353|0.497|0.0|0.0|nan
+2|2.05890350603|0.496|0.0|0.0|nan
+3|2.50962440998|0.496|0.0|0.0|nan
+4|2.78466197127|0.496|0.0|0.0|nan
+5|2.98073099104|0.496|0.0|0.0|nan
+6|3.13360058365|0.496|0.0|0.0|nan
+Model:RNN_1A Dataset:Dataset3 Loss:binary_crossentropy Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|1.49960088596|0.497|0.0|0.0|nan
+2|1.78975889865|0.496|0.0|0.0|nan
+3|2.00904154341|0.496|0.0|0.0|nan
+4|2.17954974784|0.496|0.0|0.0|nan
+5|2.31897503253|0.496|0.0|0.0|nan
+6|2.43618526993|0.496|0.0|0.0|nan
+Model:RNN_1A Dataset:Dataset3 Loss:binary_crossentropy Optimizer:rmsprop Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|1.78171199336|0.496|0.0|0.0|nan
+2|2.67403193355|0.496|0.0|0.0|nan
+3|3.77649274116|0.496|0.0|0.0|nan
+4|4.94209835504|0.496|0.0|0.0|nan
+5|6.31001176306|0.496|0.0|0.0|nan
+6|7.97950822274|0.496|0.0|0.0|nan
+Model:RNN_1A Dataset:Dataset3 Loss:mae Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.502568225354|0.496|0.0|0.0|nan
+2|0.516364194408|0.497|0.0|0.0|nan
+3|0.509399729004|0.497|0.0|0.0|nan
+4|0.510707621181|0.496|0.0|0.0|nan
+5|0.509130648957|0.496|0.0|0.0|nan
+6|0.507772420687|0.496|0.0|0.0|nan
+Model:RNN_1A Dataset:Dataset3 Loss:mae Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.509593511598|0.497|0.2|0.002|0.0039603960396
+2|0.508120514919|0.496|0.0|0.0|nan
+3|0.506712274955|0.496|0.0|0.0|nan
+4|0.506039836832|0.496|0.0|0.0|nan
+5|0.505687242136|0.496|0.0|0.0|nan
+6|0.505463882799|0.496|0.0|0.0|nan
+Model:RNN_1A Dataset:Dataset3 Loss:mae Optimizer:rmsprop Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.513528084744|0.498|0.25|0.002|0.00396825396825
+2|0.506483567777|0.496|0.0|0.0|nan
+3|0.504078777613|0.496|0.0|0.0|nan
+4|0.504081894586|0.496|0.0|0.0|nan
+5|0.504110220562|0.496|0.0|0.0|nan
+6|0.503832180075|0.496|0.0|0.0|nan
+Model:RNN_1A Dataset:Dataset3 Loss:mse Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.25214862062|0.495|0.0|0.0|nan
+2|0.321927395403|0.496|0.0|0.0|nan
+3|0.433312433363|0.496|0.0|0.0|nan
+4|0.449823088134|0.496|0.0|0.0|nan
+5|0.45998760487|0.496|0.0|0.0|nan
+6|0.466925783104|0.496|0.0|0.0|nan
+Model:RNN_1A Dataset:Dataset3 Loss:mse Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.41995672678|0.496|0.0|0.0|nan
+2|0.444305191117|0.496|0.0|0.0|nan
+3|0.45823361318|0.496|0.0|0.0|nan
+4|0.465532778376|0.496|0.0|0.0|nan
+5|0.470618498602|0.496|0.0|0.0|nan
+6|0.474316717547|0.496|0.0|0.0|nan
+Model:RNN_1A Dataset:Dataset3 Loss:mse Optimizer:rmsprop Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.436451639882|0.496|0.166666666667|0.002|0.00395256916996
+2|0.49257252978|0.496|0.0|0.0|nan
+3|0.50245488952|0.496|0.0|0.0|nan
+4|0.50385876287|0.496|0.0|0.0|nan
+5|0.503982321926|0.496|0.0|0.0|nan
+6|0.503992079763|0.496|0.0|0.0|nan
+Model:RNN_1A Dataset:Dataset3 Loss:binary_crossentropy Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|1.13359761184|0.497|0.0|0.0|nan
+2|2.09667728216|0.496|0.0|0.0|nan
+3|2.51316003093|0.496|0.0|0.0|nan
+4|2.77732622407|0.496|0.0|0.0|nan
+5|2.96941075154|0.496|0.0|0.0|nan
+6|3.12112934733|0.496|0.0|0.0|nan
+Model:RNN_1A Dataset:Dataset3 Loss:binary_crossentropy Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|1.51456285877|0.497|0.0|0.0|nan
+2|1.70115559801|0.496|0.0|0.0|nan
+3|1.83094964803|0.496|0.0|0.0|nan
+4|1.92533153342|0.496|0.0|0.0|nan
+5|1.99827050622|0.496|0.0|0.0|nan
+6|2.05808229163|0.496|0.0|0.0|nan
+Model:RNN_1A Dataset:Dataset3 Loss:binary_crossentropy Optimizer:rmsprop Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|1.54991590521|0.496|0.166666666667|0.002|0.00395256916996
+2|2.59727657265|0.496|0.0|0.0|nan
+3|3.86133283938|0.496|0.0|0.0|nan
+4|5.25267163571|0.496|0.0|0.0|nan
+5|7.16203945793|0.496|0.0|0.0|nan
+6|8.11581754447|0.496|0.0|0.0|nan
diff --git a/RNNresults/RNN_1A.Dataset4 b/RNNresults/RNN_1A.Dataset4
new file mode 100644
index 0000000..ad34e28
--- /dev/null
+++ b/RNNresults/RNN_1A.Dataset4
@@ -0,0 +1,40 @@
+Model:RNN_1A Dataset:Dataset4 Loss:mae Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0276876626564|1.0|1.0|1.0|1.0
+2|0.0044613689105|0.999|1.0|1.0|1.0
+3|0.00334507353636|0.999|1.0|1.0|1.0
+4|0.00456086230645|0.999|1.0|1.0|1.0
+5|0.00583235946379|0.999|1.0|1.0|1.0
+6|0.00512594084836|0.999|1.0|1.0|1.0
+Model:RNN_1A Dataset:Dataset4 Loss:mae Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+Model:RNN_1A Dataset:Dataset4 Loss:mae Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0345849875677|1.0|1.0|1.0|1.0
+2|0.00621722448443|0.999|1.0|1.0|1.0
+3|0.0070286212801|0.999|1.0|1.0|1.0
+4|0.00569036448422|0.999|1.0|1.0|1.0
+5|0.0095663936566|0.999|1.0|1.0|1.0
+6|0.0147276567105|0.994|0.0|0.0|nan
+Model:RNN_1A Dataset:Dataset4 Loss:mae Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+Model:RNN_1A Dataset:Dataset4 Loss:mae Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0375098753553|1.0|1.0|1.0|1.0
+2|0.00563534886693|0.999|1.0|1.0|1.0
+3|0.00528025379166|0.999|1.0|1.0|1.0
+4|0.00605825420233|0.999|1.0|1.0|1.0
+5|0.00564406192375|0.999|1.0|1.0|1.0
+6|0.00691496355015|0.999|1.0|1.0|1.0
+Model:RNN_1A Dataset:Dataset4 Loss:mae Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+Model:RNN_1A Dataset:Dataset4 Loss:mae Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0357329556849|1.0|1.0|1.0|1.0
+2|0.00608724322426|0.999|1.0|1.0|1.0
+3|0.00689868336578|0.999|1.0|1.0|1.0
+4|0.00577194050269|0.999|1.0|1.0|1.0
+5|0.00630543131611|0.999|1.0|1.0|1.0
+6|0.0129326288507|0.996|0.0|0.0|nan
+Model:RNN_1A Dataset:Dataset4 Loss:mae Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
diff --git a/RNNresults/RNN_1B.Dataset5 b/RNNresults/RNN_1B.Dataset5
new file mode 100644
index 0000000..f686aab
--- /dev/null
+++ b/RNNresults/RNN_1B.Dataset5
@@ -0,0 +1,288 @@
+Model:RNN_1B Dataset:Dataset5 Loss:mae Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.145031460188|1.0|0|0|0
+2|0.00976190251228|1.0|0|0|0
+3|0.00339465687535|1.0|0|0|0
+4|0.00261835987517|1.0|0|0|0
+5|0.0035368938639|1.0|0|0|0
+6|0.00492952826849|1.0|0|0|0
+Model:RNN_1B Dataset:Dataset5 Loss:mae Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|nan|1.0|0|0|0
+2|nan|1.0|0|0|0
+3|nan|1.0|0|0|0
+4|nan|1.0|0|0|0
+5|nan|1.0|0|0|0
+6|nan|1.0|0|0|0
+Model:RNN_1B Dataset:Dataset5 Loss:mae Optimizer:rmsprop Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.00100542886657|1.0|0|0|0
+2|nan|1.0|0|0|0
+3|nan|1.0|0|0|0
+4|nan|1.0|0|0|0
+5|nan|1.0|0|0|0
+6|nan|1.0|0|0|0
+Model:RNN_1B Dataset:Dataset5 Loss:mse Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0818018637821|1.0|0|0|0
+2|0.0335801451672|1.0|0|0|0
+3|0.0142569411848|1.0|0|0|0
+4|0.00888622078083|1.0|0|0|0
+5|0.00629438635195|1.0|0|0|0
+6|0.00495577604913|1.0|0|0|0
+Model:RNN_1B Dataset:Dataset5 Loss:mse Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|nan|1.0|0|0|0
+2|nan|1.0|0|0|0
+3|nan|1.0|0|0|0
+4|nan|1.0|0|0|0
+5|nan|1.0|0|0|0
+6|nan|1.0|0|0|0
+Model:RNN_1B Dataset:Dataset5 Loss:mse Optimizer:rmsprop Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0115138339272|0.989|0|0|0
+2|0.0128968408749|0.982|0|0|0
+3|0.00936243212615|0.982|0|0|0
+4|0.00671616539625|0.984|0|0|0
+5|0.0109657419367|0.978|0|0|0
+6|0.00950938676813|0.979|0|0|0
+Model:RNN_1B Dataset:Dataset5 Loss:categorical_crossentropy Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0571849985863|1.0|0|0|0
+2|0.0545641690276|0.996|0|0|0
+3|0.0522596172915|0.991|0|0|0
+4|0.0514062820948|0.988|0|0|0
+5|0.0587523480595|0.985|0|0|0
+6|0.0618010865787|0.981|0|0|0
+Model:RNN_1B Dataset:Dataset5 Loss:categorical_crossentropy Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|nan|1.0|0|0|0
+2|nan|1.0|0|0|0
+3|nan|1.0|0|0|0
+4|nan|1.0|0|0|0
+5|nan|1.0|0|0|0
+6|nan|1.0|0|0|0
+Model:RNN_1B Dataset:Dataset5 Loss:categorical_crossentropy Optimizer:rmsprop Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0696751659375|0.99|0|0|0
+2|0.0486732463903|0.984|0|0|0
+3|0.0612737499776|0.98|0|0|0
+4|nan|1.0|0|0|0
+5|nan|1.0|0|0|0
+6|nan|1.0|0|0|0
+Model:RNN_1B Dataset:Dataset5 Loss:mae Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.115717513651|1.0|0|0|0
+2|0.00746367261908|1.0|0|0|0
+3|0.00275493388047|1.0|0|0|0
+4|0.00198092210817|1.0|0|0|0
+5|0.00214232530582|1.0|0|0|0
+6|0.00372920926745|1.0|0|0|0
+7|0.00514224697067|1.0|0|0|0
+8|0.00353752190022|1.0|0|0|0
+9|0.00306687658343|1.0|0|0|0
+10|0.00307320806318|1.0|0|0|0
+Model:RNN_1B Dataset:Dataset5 Loss:mae Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|nan|1.0|0|0|0
+2|nan|1.0|0|0|0
+3|nan|1.0|0|0|0
+4|nan|1.0|0|0|0
+5|nan|1.0|0|0|0
+6|nan|1.0|0|0|0
+7|nan|1.0|0|0|0
+8|nan|1.0|0|0|0
+9|nan|1.0|0|0|0
+10|nan|1.0|0|0|0
+Model:RNN_1B Dataset:Dataset5 Loss:mae Optimizer:rmsprop Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|nan|1.0|0|0|0
+2|nan|1.0|0|0|0
+3|nan|1.0|0|0|0
+4|nan|1.0|0|0|0
+5|nan|1.0|0|0|0
+6|nan|1.0|0|0|0
+7|nan|1.0|0|0|0
+8|nan|1.0|0|0|0
+9|nan|1.0|0|0|0
+10|nan|1.0|0|0|0
+Model:RNN_1B Dataset:Dataset5 Loss:mse Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0832318756059|1.0|0|0|0
+2|0.0371016534083|1.0|0|0|0
+3|0.0177756639005|1.0|0|0|0
+4|0.00963010378359|1.0|0|0|0
+5|0.00637302074003|1.0|0|0|0
+6|0.00565560268775|1.0|0|0|0
+7|0.00561758703779|1.0|0|0|0
+8|0.00646091890186|0.998|0|0|0
+9|0.00767252629198|0.995|0|0|0
+10|0.00865351721879|0.993|0|0|0
+Model:RNN_1B Dataset:Dataset5 Loss:mse Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|nan|1.0|0|0|0
+2|nan|1.0|0|0|0
+3|nan|1.0|0|0|0
+4|nan|1.0|0|0|0
+5|nan|1.0|0|0|0
+6|nan|1.0|0|0|0
+7|nan|1.0|0|0|0
+8|nan|1.0|0|0|0
+9|nan|1.0|0|0|0
+10|nan|1.0|0|0|0
+Model:RNN_1B Dataset:Dataset5 Loss:mse Optimizer:rmsprop Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0126490416592|0.984|0|0|0
+2|0.00722696768265|0.985|0|0|0
+3|0.00792029268848|0.982|0|0|0
+4|0.0617031428285|0.882|0|0|0
+5|0.0101501991279|0.98|0|0|0
+6|0.0227193763965|0.955|0|0|0
+7|nan|1.0|0|0|0
+8|nan|1.0|0|0|0
+9|nan|1.0|0|0|0
+10|nan|1.0|0|0|0
+Model:RNN_1B Dataset:Dataset5 Loss:categorical_crossentropy Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0683047031199|1.0|0|0|0
+2|0.0540834993885|0.996|0|0|0
+3|0.0519524338637|0.991|0|0|0
+4|0.0514093326471|0.988|0|0|0
+5|0.0584037021525|0.987|0|0|0
+6|0.0635247824019|0.982|0|0|0
+7|0.0660818758121|0.983|0|0|0
+8|0.0647142234678|0.983|0|0|0
+9|0.0623126870643|0.986|0|0|0
+10|0.05939334863|0.987|0|0|0
+Model:RNN_1B Dataset:Dataset5 Loss:categorical_crossentropy Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|nan|1.0|0|0|0
+2|nan|1.0|0|0|0
+3|nan|1.0|0|0|0
+4|nan|1.0|0|0|0
+5|nan|1.0|0|0|0
+6|nan|1.0|0|0|0
+7|nan|1.0|0|0|0
+8|nan|1.0|0|0|0
+9|nan|1.0|0|0|0
+10|nan|1.0|0|0|0
+Model:RNN_1B Dataset:Dataset5 Loss:categorical_crossentropy Optimizer:rmsprop Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0019713893911|1.0|0|0|0
+2|nan|1.0|0|0|0
+3|nan|1.0|0|0|0
+4|nan|1.0|0|0|0
+5|nan|1.0|0|0|0
+6|nan|1.0|0|0|0
+7|nan|1.0|0|0|0
+8|nan|1.0|0|0|0
+9|nan|1.0|0|0|0
+10|nan|1.0|0|0|0
+Model:RNN_1B Dataset:Dataset5 Loss:mae Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.13483970537|1.0|0|0|0
+2|0.00992461555381|1.0|0|0|0
+3|0.00370393148781|1.0|0|0|0
+4|0.00293392593606|1.0|0|0|0
+5|0.00407249586954|1.0|0|0|0
+6|0.00519411412251|1.0|0|0|0
+7|0.00381389997003|1.0|0|0|0
+8|0.00340458013185|1.0|0|0|0
+9|0.00385008838574|1.0|0|0|0
+10|0.00214674948248|1.0|0|0|0
+Model:RNN_1B Dataset:Dataset5 Loss:mae Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|nan|1.0|0|0|0
+2|nan|1.0|0|0|0
+3|nan|1.0|0|0|0
+4|nan|1.0|0|0|0
+5|nan|1.0|0|0|0
+6|nan|1.0|0|0|0
+7|nan|1.0|0|0|0
+8|nan|1.0|0|0|0
+9|nan|1.0|0|0|0
+10|nan|1.0|0|0|0
+Model:RNN_1B Dataset:Dataset5 Loss:mae Optimizer:rmsprop Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|nan|1.0|0|0|0
+2|nan|1.0|0|0|0
+3|nan|1.0|0|0|0
+4|nan|1.0|0|0|0
+5|nan|1.0|0|0|0
+6|nan|1.0|0|0|0
+7|nan|1.0|0|0|0
+8|nan|1.0|0|0|0
+9|nan|1.0|0|0|0
+10|nan|1.0|0|0|0
+Model:RNN_1B Dataset:Dataset5 Loss:mse Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0814915379211|1.0|0|0|0
+2|0.0334279666115|1.0|0|0|0
+3|0.0147932986249|1.0|0|0|0
+4|0.00905371807951|1.0|0|0|0
+5|0.00640005423932|1.0|0|0|0
+6|0.00543209626912|1.0|0|0|0
+7|0.00514960786653|1.0|0|0|0
+8|0.00586322671729|0.998|0|0|0
+9|0.00682489221786|0.997|0|0|0
+10|0.00764595785598|0.994|0|0|0
+Model:RNN_1B Dataset:Dataset5 Loss:mse Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|nan|1.0|0|0|0
+2|nan|1.0|0|0|0
+3|nan|1.0|0|0|0
+4|nan|1.0|0|0|0
+5|nan|1.0|0|0|0
+6|nan|1.0|0|0|0
+7|nan|1.0|0|0|0
+8|nan|1.0|0|0|0
+9|nan|1.0|0|0|0
+10|nan|1.0|0|0|0
+Model:RNN_1B Dataset:Dataset5 Loss:mse Optimizer:rmsprop Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.000374601675307|1.0|0|0|0
+2|nan|1.0|0|0|0
+3|nan|1.0|0|0|0
+4|nan|1.0|0|0|0
+5|nan|1.0|0|0|0
+6|nan|1.0|0|0|0
+7|nan|1.0|0|0|0
+8|nan|1.0|0|0|0
+9|nan|1.0|0|0|0
+10|nan|1.0|0|0|0
+Model:RNN_1B Dataset:Dataset5 Loss:categorical_crossentropy Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.067757630964|1.0|0|0|0
+2|0.0529181630252|0.996|0|0|0
+3|0.0542888000888|0.991|0|0|0
+4|0.0509953980289|0.99|0|0|0
+5|0.0521964032504|0.988|0|0|0
+6|0.0550651303335|0.986|0|0|0
+7|0.0568292613548|0.983|0|0|0
+8|0.0581892355635|0.984|0|0|0
+9|0.0592586867471|0.983|0|0|0
+10|0.0585632544551|0.985|0|0|0
+Model:RNN_1B Dataset:Dataset5 Loss:categorical_crossentropy Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|nan|1.0|0|0|0
+2|nan|1.0|0|0|0
+3|nan|1.0|0|0|0
+4|nan|1.0|0|0|0
+5|nan|1.0|0|0|0
+6|nan|1.0|0|0|0
+7|nan|1.0|0|0|0
+8|nan|1.0|0|0|0
+9|nan|1.0|0|0|0
+10|nan|1.0|0|0|0
+Model:RNN_1B Dataset:Dataset5 Loss:categorical_crossentropy Optimizer:rmsprop Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.00212859313776|1.0|0|0|0
+2|nan|1.0|0|0|0
+3|nan|1.0|0|0|0
+4|nan|1.0|0|0|0
+5|nan|1.0|0|0|0
+6|nan|1.0|0|0|0
+7|nan|1.0|0|0|0
+8|nan|1.0|0|0|0
+9|nan|1.0|0|0|0
+10|nan|1.0|0|0|0
diff --git a/RNNresults/RNN_1C.Dataset0 b/RNNresults/RNN_1C.Dataset0
new file mode 100644
index 0000000..c76eda4
--- /dev/null
+++ b/RNNresults/RNN_1C.Dataset0
@@ -0,0 +1,108 @@
+Model:RNN_1C Dataset:Dataset0 Loss:mae Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0954684601948|1.0|0|0|0
+2|0.00128710299422|1.0|0|0|0
+3|0.000449766602425|1.0|0|0|0
+4|0.000261537661514|1.0|0|0|0
+5|0.000185096134999|1.0|0|0|0
+6|0.000145018334428|1.0|0|0|0
+7|0.000120739550875|1.0|0|0|0
+8|0.000104592295025|1.0|0|0|0
+9|9.31263676939e-05|1.0|0|0|0
+10|8.45795241603e-05|1.0|0|0|0
+Model:RNN_1C Dataset:Dataset0 Loss:mae Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|nan|0.0|0|0|0
+2|nan|0.0|0|0|0
+3|nan|0.0|0|0|0
+4|nan|0.0|0|0|0
+5|nan|0.0|0|0|0
+6|nan|0.0|0|0|0
+7|nan|0.0|0|0|0
+8|nan|0.0|0|0|0
+9|nan|0.0|0|0|0
+10|nan|0.0|0|0|0
+Model:RNN_1C Dataset:Dataset0 Loss:mae Optimizer:rmsprop Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|nan|0.0|0|0|0
+2|nan|0.0|0|0|0
+3|nan|0.0|0|0|0
+4|nan|0.0|0|0|0
+5|nan|0.0|0|0|0
+6|nan|0.0|0|0|0
+7|nan|0.0|0|0|0
+8|nan|0.0|0|0|0
+9|nan|0.0|0|0|0
+10|nan|0.0|0|0|0
+Model:RNN_1C Dataset:Dataset0 Loss:mse Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.0627103240564|1.0|0|0|0
+2|0.00244451830117|1.0|0|0|0
+3|0.000686242900614|1.0|0|0|0
+4|0.000351393603283|1.0|0|0|0
+5|0.00022496210142|1.0|0|0|0
+6|0.000161490891966|1.0|0|0|0
+7|0.000124227232409|1.0|0|0|0
+8|0.000100071595258|1.0|0|0|0
+9|8.33061667072e-05|1.0|0|0|0
+10|7.10744394237e-05|1.0|0|0|0
+Model:RNN_1C Dataset:Dataset0 Loss:mse Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|nan|0.0|0|0|0
+2|nan|0.0|0|0|0
+3|nan|0.0|0|0|0
+4|nan|0.0|0|0|0
+5|nan|0.0|0|0|0
+6|nan|0.0|0|0|0
+7|nan|0.0|0|0|0
+8|nan|0.0|0|0|0
+9|nan|0.0|0|0|0
+10|nan|0.0|0|0|0
+Model:RNN_1C Dataset:Dataset0 Loss:mse Optimizer:rmsprop Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.000373283533094|1.0|0|0|0
+2|nan|0.0|0|0|0
+3|nan|0.0|0|0|0
+4|nan|0.0|0|0|0
+5|nan|0.0|0|0|0
+6|nan|0.0|0|0|0
+7|nan|0.0|0|0|0
+8|nan|0.0|0|0|0
+9|nan|0.0|0|0|0
+10|nan|0.0|0|0|0
+Model:RNN_1C Dataset:Dataset0 Loss:categorical_crossentropy Optimizer:sgd Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.000953187739942|1.0|0|0|0
+2|0.000382058494419|1.0|0|0|0
+3|0.000251200921928|1.0|0|0|0
+4|0.0001953934956|1.0|0|0|0
+5|0.000164547288583|1.0|0|0|0
+6|0.000144831852915|1.0|0|0|0
+7|0.000130999293693|1.0|0|0|0
+8|0.000120645522364|1.0|0|0|0
+9|0.000112519634415|1.0|0|0|0
+10|0.000105912681531|1.0|0|0|0
+Model:RNN_1C Dataset:Dataset0 Loss:categorical_crossentropy Optimizer:adagrad Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|nan|0.0|0|0|0
+2|nan|0.0|0|0|0
+3|nan|0.0|0|0|0
+4|nan|0.0|0|0|0
+5|nan|0.0|0|0|0
+6|nan|0.0|0|0|0
+7|nan|0.0|0|0|0
+8|nan|0.0|0|0|0
+9|nan|0.0|0|0|0
+10|nan|0.0|0|0|0
+Model:RNN_1C Dataset:Dataset0 Loss:categorical_crossentropy Optimizer:rmsprop Dropout:No
+Epoch|Loss|Accuracy|Precision|Recall|Fscore
+1|0.00411484011655|1.0|0|0|0
+2|nan|0.0|0|0|0
+3|nan|0.0|0|0|0
+4|nan|0.0|0|0|0
+5|nan|0.0|0|0|0
+6|nan|0.0|0|0|0
+7|nan|0.0|0|0|0
+8|nan|0.0|0|0|0
+9|nan|0.0|0|0|0
+10|nan|0.0|0|0|0