fig fixture

code/bar_plot.py

@@ -47,7 +47,7 @@ def plot_bars(names_bars_tuple, filename='histogram_plot'):
         )
         data.append(bar)
 
-    layout = dict(xaxis=dict(title="Fixpoints", titlefont=dict(size=20)),
+    layout = dict(xaxis=dict(title="Networks", titlefont=dict(size=20)),
                   barmode='stack',
                   # height=400, width=400,
                   # margin=dict(l=20, r=20, t=20, b=20)

code/box_plots.py

@@ -79,7 +79,7 @@ def plot_box(exp: Experiment, filename='histogram_plot'):
                              tickangle=0,
                              showticklabels=True),
                   yaxis=dict(
-                      title='Occurences',
+                      title='Steps',
                       zeroline=False,
                       titlefont=dict(
                           size=30

code/line_plots.py

@@ -28,21 +28,38 @@ def line_plot(names_exp_tuple, filename='lineplot'):
 
     names, line_dict_list = names_exp_tuple
 
-    names = ['Weightwise', 'Aggregating']
+    names = ['Weightwise', 'Aggregating', 'Recurrent']
 
-    data = []
-    base_scale = cl.scales['10']['div']['RdYlGn']
-    scale = cl.interp(base_scale, len(line_dict_list) + 1)  # Map color scale to N bins
-    for ld_id, line_dict in enumerate(line_dict_list):
-        for data_point in ['ys', 'zs']:
+    if False:
+        data = []
+        base_scale = cl.scales['10']['div']['RdYlGn']
+        scale = cl.interp(base_scale, len(line_dict_list) + 1)  # Map color scale to N bins
+        for ld_id, line_dict in enumerate(line_dict_list):
+            for data_point in ['ys', 'zs']:
+                trace = go.Scatter(
+                    x=line_dict['xs'],
+                    y=line_dict[data_point],
+                    name='{} {}zero-fixpoints'.format(names[ld_id], 'non-' if data_point == 'zs' else ''),
+                    line=dict(
+                        # color=scale[ld_id],
+                        width=5,
+                        # dash='dash' if data_point == 'ys' else ''
+                    ),
+                )
+
+                data.append(trace)
+    if True:
+
+        data = []
+        base_scale = cl.scales['10']['div']['RdYlGn']
+        scale = cl.interp(base_scale, len(line_dict_list) + 1)  # Map color scale to N bins
+        for ld_id, line_dict in enumerate(line_dict_list):
             trace = go.Scatter(
                 x=line_dict['xs'],
-                y=line_dict[data_point],
-                name='{} {}zero-fixpoints'.format(names[ld_id], 'non-' if data_point == 'zs' else ''),
-                line=dict(
-                    # color=scale[ld_id],
-                    width=5,
-                    # dash='dash' if data_point == 'ys' else ''
+                y=line_dict['ys'],
+                name=names[ld_id],
+                line=dict(  # color=scale[ld_id],
+                          width=5
                 ),
             )
 

code/results/Soup/log.txt

@@ -0,0 +1 @@
+{'divergent': 0, 'fix_zero': 0, 'fix_other': 13, 'fix_sec': 0, 'other': 7}

code/results/Soup/weights.txt

@@ -0,0 +1,30 @@
+[-0.15321673  1.0428386  -0.7245892  -0.04343993  0.42338863  0.02538261
+ -0.40465942 -0.0242596  -1.226809   -0.8168446   0.26588777 -1.0929432
+  0.5383322  -0.73875046]
+[-0.03072096 -1.369665   -0.357126   -0.21180922  0.3853204   0.22853081
+ -0.3705557  -0.21977347 -0.6684716   0.12849599  1.0226644  -0.0922638
+ -0.7828449  -0.6572327 ]
+[-1.2444692   0.61213857  0.07965802  0.12361202  0.62641835  0.9720597
+  0.3863232   0.59948945  1.0857513   0.49231085 -0.5319295   0.29433587
+ -0.64177823  0.17603302]
+[-0.9938292  -0.4438207  -0.03172896  0.06261964 -0.3870194   0.7637992
+  0.0244509  -0.04825407  0.91551745 -0.78740424  0.29226422 -0.52767307
+ -0.41744384  0.5567152 ]
+[-0.39049304  0.8842579  -0.8447943  -0.19669186  0.7207061   0.16780053
+  0.3728221   0.08680353  0.7535456  -0.1000197   0.02029054  0.8640245
+ -0.15881588  1.1905665 ]
+[ 1.0482084   0.9248296  -0.26946014  0.57047915 -0.32660747  0.6914731
+ -0.18025818  0.3816289  -0.69358927  0.21312684 -0.39932403 -0.02991759
+ -0.83068466  0.45619962]
+[ 0.75814664  0.10328437  0.07867077 -0.0743314  -0.53440267  0.50492585
+ -0.54172474  0.51184535  0.3462249   1.0527638  -0.9503541   0.9235086
+ -0.1665241   1.1497779 ]
+[-0.77187353  1.1105504   0.24265823  0.53782856 -0.34098852 -0.75576884
+ -0.25396293 -0.56288165  0.3851537  -0.67497945  0.14336896  0.763481
+ -0.9224985   0.6374753 ]
+[-0.79123825  0.68166596 -0.30061013 -0.19360289  0.5632736   0.36276665
+  0.7470975   0.48115698  0.10046808 -0.8064349  -1.036736   -0.68296516
+ -1.156437    0.52633154]
+[ 0.1788832  -1.5321186  -0.62001514 -0.3870902   0.97524184  0.6088638
+ -0.08297889 -0.05180515 -0.29096788  0.7519439   0.8803648   0.82771575
+ -0.854887    0.1742936 ]

code/results/exp-applying_fixpoint-_1552681870.3570378-0/log.txt

@@ -0,0 +1,12 @@
+WeightwiseNeuralNetwork activiation='linear' use_bias=False
+{'divergent': 23, 'fix_zero': 27, 'fix_other': 0, 'fix_sec': 0, 'other': 0}
+
+
+AggregatingNeuralNetwork activiation='linear' use_bias=False
+{'divergent': 4, 'fix_zero': 46, 'fix_other': 0, 'fix_sec': 0, 'other': 0}
+
+
+RecurrentNeuralNetwork activiation='linear' use_bias=False
+{'divergent': 46, 'fix_zero': 4, 'fix_other': 0, 'fix_sec': 0, 'other': 0}
+
+

code/results/exp-learn-from-soup-_1552658566.5572753-0/log.txt

@@ -0,0 +1,4 @@
+TrainingNeuralNetworkDecorator activiation='linear' use_bias=False
+{'xs': [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100], 'ys': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'zs': [0.0, 1.2, 5.2, 7.4, 8.1, 9.1, 9.6, 9.8, 10.0, 9.9, 9.9]}
+
+

code/results/exp-mixed-self-fixpoints-_1552666977.5858653-0/log.txt

@@ -0,0 +1,12 @@
+WeightwiseNeuralNetwork activiation='linear' use_bias=False
+{'xs': [0, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500], 'ys': [0.2, 0.3, 0.15, 0.55, 0.7, 0.85, 0.8, 0.95, 0.9, 1.0, 1.0]}
+
+
+AggregatingNeuralNetwork activiation='linear' use_bias=False
+{'xs': [0, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500], 'ys': [1.0, 0.95, 1.0, 1.0, 0.95, 0.9, 0.8, 1.0, 0.85, 1.0, 0.9]}
+
+
+RecurrentNeuralNetwork activiation='linear' use_bias=False
+{'xs': [0, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500], 'ys': [0.05, 0.0, 0.05, 0.0, 0.0, 0.1, 0.1, 0.05, 0.1, 0.0, 0.0]}
+
+

code/results/exp-mixed-soup-_1552674483.9866457-0/log.txt

@@ -0,0 +1,8 @@
+TrainingNeuralNetworkDecorator activiation='linear' use_bias=False
+{'xs': [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100], 'ys': [0.0, 0.0, 0.1, 0.0, 0.0, 0.0, 0.0, 0.1, 0.0, 0.0, 0.0], 'zs': [0.0, 0.0, 0.7, 1.9, 3.6, 4.3, 6.0, 6.1, 8.3, 7.7, 8.8]}
+
+
+TrainingNeuralNetworkDecorator activiation='linear' use_bias=False
+{'xs': [0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100], 'ys': [0.8, 0.4, 0.4, 0.3, 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3], 'zs': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]}
+
+

code/results/exp-training_fixpoint-_1552658296.0913951-0/log.txt

@@ -0,0 +1,12 @@
+WeightwiseNeuralNetwork activiation='linear' use_bias=False
+{'divergent': 0, 'fix_zero': 0, 'fix_other': 50, 'fix_sec': 0, 'other': 0}
+
+
+AggregatingNeuralNetwork activiation='linear' use_bias=False
+{'divergent': 0, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 50}
+
+
+RecurrentNeuralNetwork activiation='linear' use_bias=False
+{'divergent': 38, 'fix_zero': 0, 'fix_other': 0, 'fix_sec': 0, 'other': 12}
+
+

code/results/self_application_aggregation_network/log.txt

@@ -0,0 +1 @@
+{'divergent': 0, 'fix_zero': 10, 'fix_other': 0, 'fix_sec': 0, 'other': 0}

code/setups/known-fixpoint-variation.py

@@ -28,7 +28,8 @@ def generate_fixpoint_weights():
 def generate_fixpoint_net():
     #NOTE: Weightwise only is all we can do right now IMO
     net = WeightwiseNeuralNetwork(width=2, depth=2).with_keras_params(activation='sigmoid')
-    # net = AggregatingNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation='sigmoid') # I don't know if this work for aggregaeting. We don't actually need it, though.
+    # I don't know if this work for aggregaeting. We don't actually need it, though.
+    # net = AggregatingNeuralNetwork(aggregates=4, width=2, depth=2).with_keras_params(activation='sigmoid')
     net.set_weights(generate_fixpoint_weights())
     return net