[pre-commit.ci] auto fixes from pre-commit.com hooks

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Author: pre-commit-ci[bot]

app.json

@@ -5,4 +5,3 @@
     }
   }
 }
-

datasets/baseballdb/README.txt

@@ -10,13 +10,11 @@ Chadwick Baseball Bureau (http://www.chadwick-bureau.com),
 from its Register of baseball personnel.
 
 Player performance data for 1871 through 2014 is based on the
-Lahman Baseball Database, version 2015-01-24, which is 
+Lahman Baseball Database, version 2015-01-24, which is
 Copyright (C) 1996-2015 by Sean Lahman.
 
 The tables Parks.csv and HomeGames.csv are based on the game logs
 and park code table published by Retrosheet.
 This information is available free of charge from and is copyrighted
-by Retrosheet.  Interested parties may contact Retrosheet at 
+by Retrosheet.  Interested parties may contact Retrosheet at
 http://www.retrosheet.org.
-
-

datasets/baseballdb/core/AwardsManagers.csv

@@ -176,5 +176,5 @@ showabu99,BBWAA Manager of the Year,2014,AL,,
 willima04,BBWAA Manager of the Year,2014,NL,,
 banisje01,BBWAA Manager of the Year,2015,AL,,
 maddojo99,BBWAA Manager of the Year,2015,NL,,
-francte01,BBWAA Manager of the Year,2016,AL,,	
+francte01,BBWAA Manager of the Year,2016,AL,,
 roberda07,BBWAA Manager of the Year,2016,NL,,

datasets/baseballdb/core/readme2014.txt

@@ -11,14 +11,14 @@ Rua Dr. Roberto Frias, 378
 
 
 =========================================
-Background 
+Background
 =========================================
 
-Bike sharing systems are new generation of traditional bike rentals where whole process from membership, rental and return 
-back has become automatic. Through these systems, user is able to easily rent a bike from a particular position and return 
-back at another position. Currently, there are about over 500 bike-sharing programs around the world which is composed of 
-over 500 thousands bicycles. Today, there exists great interest in these systems due to their important role in traffic, 
-environmental and health issues. 
+Bike sharing systems are new generation of traditional bike rentals where whole process from membership, rental and return
+back has become automatic. Through these systems, user is able to easily rent a bike from a particular position and return
+back at another position. Currently, there are about over 500 bike-sharing programs around the world which is composed of
+over 500 thousands bicycles. Today, there exists great interest in these systems due to their important role in traffic,
+environmental and health issues.
 
 Apart from interesting real world applications of bike sharing systems, the characteristics of data being generated by
 these systems make them attractive for the research. Opposed to other transport services such as bus or subway, the duration
@@ -30,21 +30,21 @@ events in the city could be detected via monitoring these data.
 Data Set
 =========================================
 Bike-sharing rental process is highly correlated to the environmental and seasonal settings. For instance, weather conditions,
-precipitation, day of week, season, hour of the day, etc. can affect the rental behaviors. The core data set is related to  
-the two-year historical log corresponding to years 2011 and 2012 from Capital Bikeshare system, Washington D.C., USA which is 
-publicly available in http://capitalbikeshare.com/system-data. We aggregated the data on two hourly and daily basis and then 
-extracted and added the corresponding weather and seasonal information. Weather information are extracted from http://www.freemeteo.com. 
+precipitation, day of week, season, hour of the day, etc. can affect the rental behaviors. The core data set is related to
+the two-year historical log corresponding to years 2011 and 2012 from Capital Bikeshare system, Washington D.C., USA which is
+publicly available in http://capitalbikeshare.com/system-data. We aggregated the data on two hourly and daily basis and then
+extracted and added the corresponding weather and seasonal information. Weather information are extracted from http://www.freemeteo.com.
 
 =========================================
 Associated tasks
 =========================================
 
-	- Regression: 
+	- Regression:
 		Predication of bike rental count hourly or daily based on the environmental and seasonal settings.
-	
-	- Event and Anomaly Detection:  
+
+	- Event and Anomaly Detection:
 		Count of rented bikes are also correlated to some events in the town which easily are traceable via search engines.
-		For instance, query like "2012-10-30 washington d.c." in Google returns related results to Hurricane Sandy. Some of the important events are 
+		For instance, query like "2012-10-30 washington d.c." in Google returns related results to Hurricane Sandy. Some of the important events are
 		identified in [1]. Therefore the data can be used for validation of anomaly or event detection algorithms as well.
 
 
@@ -56,12 +56,12 @@ Files
 	- hour.csv : bike sharing counts aggregated on hourly basis. Records: 17379 hours
 	- day.csv - bike sharing counts aggregated on daily basis. Records: 731 days
 
-	
+
 =========================================
 Dataset characteristics
-=========================================	
+=========================================
 Both hour.csv and day.csv have the following fields, except hr which is not available in day.csv
-	
+
 	- instant: record index
 	- dteday : date
 	- season : season (1:springer, 2:summer, 3:fall, 4:winter)
@@ -71,7 +71,7 @@ Both hour.csv and day.csv have the following fields, except hr which is not avai
 	- holiday : weather day is holiday or not (extracted from http://dchr.dc.gov/page/holiday-schedule)
 	- weekday : day of the week
 	- workingday : if day is neither weekend nor holiday is 1, otherwise is 0.
-	+ weathersit : 
+	+ weathersit :
 		- 1: Clear, Few clouds, Partly cloudy, Partly cloudy
 		- 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist
 		- 3: Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds
@@ -83,7 +83,7 @@ Both hour.csv and day.csv have the following fields, except hr which is not avai
 	- casual: count of casual users
 	- registered: count of registered users
 	- cnt: count of total rental bikes including both casual and registered
-	
+
 =========================================
 License
 =========================================
@@ -107,5 +107,5 @@ Use of this dataset in publications must be cited to the following publication:
 =========================================
 Contact
 =========================================
-	
+
 For further information about this dataset please contact Hadi Fanaee-T (hadi.fanaee@fe.up.pt)

datasets/bikes/Readme.txt

@@ -94,4 +94,4 @@ experiment,isolate,ST,OD600,measurement,replicate,normalized_measurement
 2,13,4,0.522,1.316,3,2.521072797
 2,14,4,0.576,1.959,3,3.401041667
 2,15,4,0.427,1.073,3,2.512880562
-2,ATCC_29212,30,0.688,1.122,3,1.630813953
+2,ATCC_29212,30,0.688,1.122,3,1.630813953

datasets/biofilm.csv

@@ -118,4 +118,4 @@ Season,Team,Team Salary,Team Salary (in millions),League,Wins,Losses,Winning %,A
 2013,Tampa Bay Rays,57030272,57,AL,92,71,0.564,5538,700,1421,296,23,165,670,589,1171,73,38,0.257,0.329,0.408,0.737,3.74,42,60,1464,1315,646,608,153,482,1310,0.24,1.23,13176,6044,4392,1593,59,147,0.783,69,0.99,0.708
 2013,Texas Rangers,127197575,127.2,AL,91,72,0.558,5585,730,1465,262,23,176,691,462,1067,149,46,0.262,0.323,0.412,0.735,3.62,46,57,1463.1,1370,636,589,157,498,1309,0.248,1.28,13170,6025,4390,1549,86,146,0.682,68,0.986,0.695
 2013,Toronto Blue Jays,118244039,118.2,AL,74,88,0.457,5537,712,1398,273,24,185,669,510,1123,112,41,0.252,0.318,0.411,0.729,4.25,39,58,1452,1451,756,685,195,500,1208,0.259,1.34,13068,6072,4356,1605,111,145,0.75,54,0.982,0.691
-2013,Washington Nationals,112431770,112.4,NL,86,76,0.531,5436,656,1365,259,27,161,621,464,1192,88,28,0.251,0.313,0.398,0.71,3.59,47,68,1445.2,1367,626,576,142,405,1236,0.249,1.23,13011,5993,4337,1549,107,146,0.826,43,0.982,0.691
+2013,Washington Nationals,112431770,112.4,NL,86,76,0.531,5436,656,1365,259,27,161,621,464,1192,88,28,0.251,0.313,0.398,0.71,3.59,47,68,1445.2,1367,626,576,142,405,1236,0.249,1.23,13011,5993,4337,1549,107,146,0.826,43,0.982,0.691

datasets/mlb_2013-2016.csv

@@ -32,4 +32,4 @@ type,yr_construction,period_op,months,n_damages
 5,2,2,437,7
 5,3,1,1157,5
 5,3,2,2161,12
-5,4,2,542,1
+5,4,2,542,1

datasets/ship-damage.txt

@@ -115,4 +115,4 @@ def plot_elbo(self):
         plt.plot(-self.advi_hist)
         plt.ylabel('ELBO')
         plt.xlabel('iteration')
-        sns.despine()
+        sns.despine()

models/__init__.py

@@ -6,33 +6,33 @@
 
 
 class ForestCoverModel(BayesianModel):
-    
+
     def __init__(self, n_hidden):
         super(ForestCoverModel, self).__init__()
         self.n_hidden = n_hidden
 
     def create_model(self, X=None, y=None):
         if X:
             num_samples, self.num_pred = X.shape
-            
+
         if y:
             num_samples, self.num_out = Y.shape
 
         model_input = theano.shared(np.zeros(shape=(1, self.num_pred)))
         model_output = theano.shared(np.zeros(shape=(1,self.num_out)))
-        
+
         self.shared_vars = {
             'model_input': model_input,
             'model_output': model_output
         }
-        
+
         with pm.Model() as model:
             # Define weights
-            weights_1 = pm.Normal('w_1', mu=0, sd=1, 
+            weights_1 = pm.Normal('w_1', mu=0, sd=1,
                                   shape=(self.num_pred, self.n_hidden))
             weights_2 = pm.Normal('w_2', mu=0, sd=1,
                                   shape=(self.n_hidden, self.n_hidden))
-            weights_out = pm.Normal('w_out', mu=0, sd=1, 
+            weights_out = pm.Normal('w_out', mu=0, sd=1,
                                     shape=(self.n_hidden, self.num_outs))
 
             # Define activations
@@ -41,29 +41,27 @@ def create_model(self, X=None, y=None):
             acts_out = tt.nnet.softmax(tt.dot(acts_2, weights_out))  # noqa
 
             # Define likelihood
-            out = pm.Multinomial('likelihood', n=1, p=acts_out, 
+            out = pm.Multinomial('likelihood', n=1, p=acts_out,
                                  observed=model_output)
-            
+
         return model
-    
-    
+
+
     def fit(self, X, y, n=200000, batch_size=10):
         """
         Train the Bayesian NN model.
         """
         num_samples, self.num_pred = X.shape
         _, self.num_out = y.shape
-        
+
         if self.cached_model is None:
             self.cached_model = self.create_model()
-            
+
         with self.cached_model:
             minibatches = {
                 self.shared_vars['model_input']: pm.Minibatch(X, batch_size=batch_size),
                 self.shared_vars['model_output']: pm.Minibatch(y, batch_size=batch_size),
             }
             self._inference(minibatches, n)
-            
+
         return self
-    
-    

models/feedforward.py

@@ -21,4 +21,3 @@
     print('Used the cpu')
 else:
     print('Used the gpu')
-