enforce files end in a newline and only a newline

docs/Makefile

@@ -16,4 +16,4 @@ help:
 # Catch-all target: route all unknown targets to Sphinx using the new
 # "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
 %: Makefile
-	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/numpy_ml.bandits.rst

@@ -9,4 +9,3 @@ Multi-armed bandits
    numpy_ml.bandits.policies
 
    numpy_ml.bandits.trainer
-

docs/numpy_ml.hmm.rst

@@ -2,7 +2,7 @@
 Hidden Markov models
 ####################
 
-A `hidden Markov model`_ (HMM) is a generative model for sequences of observations. 
+A `hidden Markov model`_ (HMM) is a generative model for sequences of observations.
 
 .. _`hidden Markov model` : https://en.wikipedia.org/wiki/Hidden_Markov_model
 
@@ -58,7 +58,7 @@ the EM algorithm is known as the `forward-backward`_ / `Baum-Welch algorithm`_.
 - :class:`~numpy_ml.hmm.MultinomialHMM`
 
 **References**
-    
+
 .. [1] Ghahramani, Z. (2001). "An Intro to HMMs and Bayesian networks".
        *International Journal of Pattern Recognition and AI, 15(1)*: 9-42.
 
@@ -67,4 +67,3 @@ the EM algorithm is known as the `forward-backward`_ / `Baum-Welch algorithm`_.
    :hidden:
 
    numpy_ml.hmm.MultinomialHMM
-

docs/numpy_ml.neural_nets.activations.rst

@@ -1,4 +1,4 @@
-Activations 
+Activations
 ===========
 
 Popular (and some not-so-popular) activation functions for use within arbitrary
@@ -80,4 +80,3 @@ neural networks.
     :members:
     :undoc-members:
     :inherited-members:
-

docs/numpy_ml.neural_nets.models.vae.rst

@@ -7,4 +7,3 @@ Variational auto-encoder (``neural_nets.models.vae``)
     :undoc-members:
     :inherited-members:
     :show-inheritance:
-

docs/numpy_ml.neural_nets.modules.rst

@@ -1,4 +1,4 @@
-Modules 
+Modules
 ========
 
 ``BidirectionalLSTM``
@@ -30,4 +30,3 @@ Modules
 .. autoclass:: numpy_ml.neural_nets.modules.WavenetResidualModule
     :members:
     :undoc-members:
-

docs/numpy_ml.neural_nets.optimizers.rst

@@ -1,4 +1,4 @@
-Optimizers 
+Optimizers
 ===========
 Popular gradient-based strategies for optimizing parameters in neural networks.
 
@@ -43,4 +43,3 @@ found via different optimization strategies, see:
     :members:
     :undoc-members:
     :show-inheritance:
-

docs/numpy_ml.neural_nets.rst

@@ -1,7 +1,7 @@
-Neural networks 
+Neural networks
 ###############
 The neural network module includes common building blocks for implementing
-modern `deep learning`_ models. 
+modern `deep learning`_ models.
 
 .. _`deep learning`: https://en.wikipedia.org/wiki/Deep_learning
 
@@ -225,5 +225,3 @@ arithmetic, padding, and minibatching.
    numpy_ml.neural_nets.models
 
    numpy_ml.neural_nets.utils
-
-

docs/numpy_ml.ngram.mle.rst

@@ -5,4 +5,3 @@
         :members:
 	:undoc-members:
 	:inherited-members:
-

docs/numpy_ml.preprocessing.general.rst

@@ -1,4 +1,4 @@
-General 
+General
 #######
 
 ``FeatureHasher``
@@ -30,4 +30,3 @@ General
 
 .. automodule:: numpy_ml.preprocessing.general
 	:members: minibatch
-

docs/numpy_ml.utils.rst

@@ -1,4 +1,4 @@
-Utilities 
+Utilities
 #########
 
 .. toctree::
@@ -15,4 +15,3 @@ Utilities
    numpy_ml.utils.windows
 
    numpy_ml.utils.testing
-

numpy_ml/bandits/README.md

@@ -1,9 +1,9 @@
 # Bandits
-The `bandit.py` module includes several simple multi-arm bandit 
+The `bandit.py` module includes several simple multi-arm bandit
 environments.
 
 The `policies.py` module implements a number of standard multi-arm bandit
-policies. 
+policies.
 
 1. **Bandits**
     - MAB: Bernoulli, Multinomial, and Gaussian payout distributions
@@ -21,4 +21,3 @@ policies.
 
 <img src="img/EpsilonGreedy.png" align='center' height="400" />
 </p>
-

numpy_ml/neural_nets/modules/README.md

@@ -1,11 +1,10 @@
 # Modules
 
 The `modules.py` module implements common multi-layer blocks that appear across
-many modern deep networks. It includes: 
+many modern deep networks. It includes:
 
 - Bidirectional LSTMs ([Schuster & Paliwal, 1997](https://pdfs.semanticscholar.org/4b80/89bc9b49f84de43acc2eb8900035f7d492b2.pdf))
 - ResNet-style "identity" (i.e., `same`-convolution) residual blocks ([He et al., 2015](https://arxiv.org/pdf/1512.03385.pdf))
 - ResNet-style "convolutional" (i.e., parametric) residual blocks ([He et al., 2015](https://arxiv.org/pdf/1512.03385.pdf))
 - WaveNet-style residual block with dilated causal convolutions ([van den Oord et al., 2016](https://arxiv.org/pdf/1609.03499.pdf))
 - Transformer-style multi-headed dot-product attention ([Vaswani et al., 2017](https://arxiv.org/pdf/1706.03762.pdf))
-

numpy_ml/ngram/README.md

@@ -12,4 +12,4 @@ The `ngram.py` module implements [n-gram models](https://en.wikipedia.org/wiki/N
 </p>
 <p align="center">
   <img src="img/add_smooth.png" height="550" />
-</p>
+</p>

numpy_ml/rl_models/README.md

@@ -3,7 +3,7 @@ The `agents.py` module implements a number of standard reinforcement learning (R
 can be run on [OpenAI gym](https://gym.openai.com/) environments.
 
 1. **Monte Carlo Methods**
-    - First-visit Monte Carlo updates (on-policy) 
+    - First-visit Monte Carlo updates (on-policy)
     - Incremental weighted importance sampling (off-policy)
     - Cross-entropy method ([Mannor, Rubinstein, & Gat, 2003](https://www.aaai.org/Papers/ICML/2003/ICML03-068.pdf))
 
@@ -24,4 +24,3 @@ can be run on [OpenAI gym](https://gym.openai.com/) environments.
 
 <img src="img/DynaAgent-Taxi-v2.png" align='center' height="400" />
 </p>
-