Update requirements

.circleci/config.yml

@@ -7,8 +7,8 @@ jobs:
   build_and_test:
     docker:
       # specify the version you desire here
-      # use `-browsers` prefix for selenium tests, e.g. `3.6.1-browsers`
-      - image: circleci/python:3.6.1
+      # use `-browsers` prefix for selenium tests, e.g. `3.7.3-browsers`
+      - image: circleci/python:3.7.3
 
       # Specify service dependencies here if necessary
       # CircleCI maintains a library of pre-built images
@@ -17,15 +17,18 @@ jobs:
 
     working_directory: ~/paysage
 
+    environment:
+      CIRCLE_REPOSITORY_URL: https://github.com/drckf/paysage
+
     steps:
       - checkout
 
       # Download and cache dependencies
       - restore_cache:
           keys:
-          - v2-dependencies-{{ .Branch }}-{{ checksum "setup.py" }}
+          - v4-dependencies-{{ .Branch }}-{{ checksum "setup.py" }}
           # fallback to using the latest cache if no exact match is found
-          - v2-dependencies-
+          - v4-dependencies-
 
       - run:
           name: Install Requirements
@@ -38,7 +41,7 @@ jobs:
       - save_cache:
           paths:
             - "venv"
-          key: v2-dependencies-{{ .Branch }}-{{ checksum "setup.py" }}
+          key: v4-dependencies-{{ .Branch }}-{{ checksum "setup.py" }}
 
       # run tests on the python backend
       - run:

docs/backends/nonlinearity.md

@@ -5,7 +5,7 @@
 ### acosh
 ```py
 
-def acosh(x: Union[torch.FloatTensor, torch.cuda.FloatTensor]) -> Union[torch.FloatTensor, torch.cuda.FloatTensor]
+def acosh(x: numpy.ndarray) -> numpy.ndarray
 
 ```
 
@@ -17,7 +17,7 @@ Elementwise inverse hyperbolic cosine of a tensor.<br /><br />Args:<br />&nbsp;&
 ### atanh
 ```py
 
-def atanh(x: Union[torch.FloatTensor, torch.cuda.FloatTensor]) -> Union[torch.FloatTensor, torch.cuda.FloatTensor]
+def atanh(x: numpy.ndarray) -> numpy.ndarray
 
 ```
 
@@ -29,7 +29,7 @@ Elementwise inverse hyperbolic tangent of a tensor.<br /><br />Args:<br />&nbsp;
 ### cos
 ```py
 
-def cos(x: Union[torch.FloatTensor, torch.cuda.FloatTensor]) -> Union[torch.FloatTensor, torch.cuda.FloatTensor]
+def cos(x: numpy.ndarray) -> numpy.ndarray
 
 ```
 
@@ -41,7 +41,7 @@ Elementwise cosine of a tensor.<br /><br />Args:<br />&nbsp;&nbsp;&nbsp;&nbsp;x:
 ### cosh
 ```py
 
-def cosh(x: Union[torch.FloatTensor, torch.cuda.FloatTensor]) -> Union[torch.FloatTensor, torch.cuda.FloatTensor]
+def cosh(x: numpy.ndarray) -> numpy.ndarray
 
 ```
 
@@ -53,7 +53,7 @@ Elementwise hyperbolic cosine of a tensor.<br /><br />Args:<br />&nbsp;&nbsp;&nb
 ### exp
 ```py
 
-def exp(x: Union[torch.FloatTensor, torch.cuda.FloatTensor]) -> Union[torch.FloatTensor, torch.cuda.FloatTensor]
+def exp(x: numpy.ndarray) -> numpy.ndarray
 
 ```
 
@@ -65,7 +65,7 @@ Elementwise exponential function of a tensor.<br /><br />Args:<br />&nbsp;&nbsp;
 ### expit
 ```py
 
-def expit(x: Union[torch.FloatTensor, torch.cuda.FloatTensor]) -> Union[torch.FloatTensor, torch.cuda.FloatTensor]
+def expit(x: numpy.ndarray) -> numpy.ndarray
 
 ```
 
@@ -77,7 +77,7 @@ Elementwise expit (a.k.a. logistic) function of a tensor.<br /><br />Args:<br />
 ### log
 ```py
 
-def log(x: Union[torch.FloatTensor, torch.cuda.FloatTensor]) -> Union[torch.FloatTensor, torch.cuda.FloatTensor]
+def log(x: numpy.ndarray) -> numpy.ndarray
 
 ```
 
@@ -89,7 +89,7 @@ Elementwise natural logarithm of a tensor.<br /><br />Args:<br />&nbsp;&nbsp;&nb
 ### logaddexp
 ```py
 
-def logaddexp(x1: Union[torch.FloatTensor, torch.cuda.FloatTensor], x2: Union[torch.FloatTensor, torch.cuda.FloatTensor]) -> Union[torch.FloatTensor, torch.cuda.FloatTensor]
+def logaddexp(x1: numpy.ndarray, x2: numpy.ndarray) -> numpy.ndarray
 
 ```
 
@@ -101,7 +101,7 @@ Elementwise logaddexp function: log(exp(x1) + exp(x2))<br /><br />Args:<br />&nb
 ### logcosh
 ```py
 
-def logcosh(x: Union[torch.FloatTensor, torch.cuda.FloatTensor]) -> Union[torch.FloatTensor, torch.cuda.FloatTensor]
+def logcosh(x: numpy.ndarray) -> numpy.ndarray
 
 ```
 
@@ -113,7 +113,7 @@ Elementwise logarithm of the hyperbolic cosine of a tensor.<br /><br />Args:<br
 ### logit
 ```py
 
-def logit(x: Union[torch.FloatTensor, torch.cuda.FloatTensor]) -> Union[torch.FloatTensor, torch.cuda.FloatTensor]
+def logit(x: numpy.ndarray) -> numpy.ndarray
 
 ```
 
@@ -125,7 +125,7 @@ Elementwise logit function of a tensor. Inverse of the expit function.<br /><br
 ### normal\_pdf
 ```py
 
-def normal_pdf(x: Union[torch.FloatTensor, torch.cuda.FloatTensor]) -> Union[torch.FloatTensor, torch.cuda.FloatTensor]
+def normal_pdf(x: numpy.ndarray) -> numpy.ndarray
 
 ```
 
@@ -137,7 +137,7 @@ Elementwise probability density function of the standard normal distribution.<br
 ### reciprocal
 ```py
 
-def reciprocal(x: Union[torch.FloatTensor, torch.cuda.FloatTensor]) -> Union[torch.FloatTensor, torch.cuda.FloatTensor]
+def reciprocal(x: numpy.ndarray) -> numpy.ndarray
 
 ```
 
@@ -149,7 +149,7 @@ Elementwise inverse of a tensor.<br /><br />Args:<br />&nbsp;&nbsp;&nbsp;&nbsp;x
 ### sin
 ```py
 
-def sin(x: Union[torch.FloatTensor, torch.cuda.FloatTensor]) -> Union[torch.FloatTensor, torch.cuda.FloatTensor]
+def sin(x: numpy.ndarray) -> numpy.ndarray
 
 ```
 
@@ -161,19 +161,19 @@ Elementwise sine of a tensor.<br /><br />Args:<br />&nbsp;&nbsp;&nbsp;&nbsp;x: A
 ### softmax
 ```py
 
-def softmax(x: Union[numpy.ndarray, torch.IntTensor, torch.cuda.IntTensor, torch.ShortTensor, torch.cuda.ShortTensor, torch.LongTensor, torch.cuda.LongTensor, torch.ByteTensor, torch.cuda.ByteTensor, torch.FloatTensor, torch.cuda.FloatTensor, torch.DoubleTensor, torch.cuda.DoubleTensor], axis: int=1) -> Union[numpy.ndarray, torch.IntTensor, torch.cuda.IntTensor, torch.ShortTensor, torch.cuda.ShortTensor, torch.LongTensor, torch.cuda.LongTensor, torch.ByteTensor, torch.cuda.ByteTensor, torch.FloatTensor, torch.cuda.FloatTensor, torch.DoubleTensor, torch.cuda.DoubleTensor]
+def softmax(x: numpy.ndarray, axis: int = 1) -> numpy.ndarray
 
 ```
 
 
 
-Softmax function on a tensor.<br />Exponentiaties the tensor elementwise and divides<br />&nbsp;&nbsp;&nbsp;&nbsp;by the sum along axis.<br /><br />Args:<br />&nbsp;&nbsp;&nbsp;&nbsp;x: A tensor.<br /><br />Returns:<br />&nbsp;&nbsp;&nbsp;&nbsp;tensor: Softmax of the tensor.
+Softmax function on a tensor.<br />Exponentiaties the tensor elementwise and divides<br />&nbsp;&nbsp;&nbsp;&nbsp;by the sum along axis=1.<br /><br />Args:<br />&nbsp;&nbsp;&nbsp;&nbsp;x: A tensor.<br /><br />Returns:<br />&nbsp;&nbsp;&nbsp;&nbsp;tensor: Softmax of the tensor.
 
 
 ### softplus
 ```py
 
-def softplus(x: Union[torch.FloatTensor, torch.cuda.FloatTensor]) -> Union[torch.FloatTensor, torch.cuda.FloatTensor]
+def softplus(x: numpy.ndarray) -> numpy.ndarray
 
 ```
 
@@ -185,7 +185,7 @@ Elementwise softplus function of a tensor.<br /><br />Args:<br />&nbsp;&nbsp;&nb
 ### sqrt
 ```py
 
-def sqrt(x: Union[torch.FloatTensor, torch.cuda.FloatTensor]) -> Union[torch.FloatTensor, torch.cuda.FloatTensor]
+def sqrt(x: numpy.ndarray) -> numpy.ndarray
 
 ```
 
@@ -197,7 +197,7 @@ Elementwise square root of a tensor.<br /><br />Args:<br />&nbsp;&nbsp;&nbsp;&nb
 ### square
 ```py
 
-def square(x: Union[torch.FloatTensor, torch.cuda.FloatTensor]) -> Union[torch.FloatTensor, torch.cuda.FloatTensor]
+def square(x: numpy.ndarray) -> numpy.ndarray
 
 ```
 
@@ -209,7 +209,7 @@ Elementwise square of a tensor.<br /><br />Args:<br />&nbsp;&nbsp;&nbsp;&nbsp;x:
 ### tabs
 ```py
 
-def tabs(x: Union[torch.FloatTensor, torch.cuda.FloatTensor]) -> Union[torch.FloatTensor, torch.cuda.FloatTensor]
+def tabs(x: numpy.ndarray) -> numpy.ndarray
 
 ```
 
@@ -221,7 +221,7 @@ Elementwise absolute value of a tensor.<br /><br />Args:<br />&nbsp;&nbsp;&nbsp;
 ### tanh
 ```py
 
-def tanh(x: Union[torch.FloatTensor, torch.cuda.FloatTensor]) -> Union[torch.FloatTensor, torch.cuda.FloatTensor]
+def tanh(x: numpy.ndarray) -> numpy.ndarray
 
 ```
 
@@ -233,7 +233,7 @@ Elementwise hyperbolic tangent of a tensor.<br /><br />Args:<br />&nbsp;&nbsp;&n
 ### tmul
 ```py
 
-def tmul(a: Union[int, float], x: Union[torch.FloatTensor, torch.cuda.FloatTensor]) -> Union[torch.FloatTensor, torch.cuda.FloatTensor]
+def tmul(a: Union[int, float], x: numpy.ndarray) -> numpy.ndarray
 
 ```
 
@@ -245,19 +245,19 @@ Elementwise multiplication of tensor x by scalar a.<br /><br />Args:<br />&nbsp;
 ### tmul\_
 ```py
 
-def tmul_(a: Union[int, float], x: Union[torch.FloatTensor, torch.cuda.FloatTensor]) -> Union[torch.FloatTensor, torch.cuda.FloatTensor]
+def tmul_(a: Union[int, float], x: numpy.ndarray)
 
 ```
 
 
 
-Elementwise multiplication of tensor x by scalar a.<br /><br />Notes:<br />&nbsp;&nbsp;&nbsp;&nbsp;Modifes x in place<br /><br />Args:<br />&nbsp;&nbsp;&nbsp;&nbsp;x: A tensor.<br />&nbsp;&nbsp;&nbsp;&nbsp;a: scalar.<br /><br />Returns:<br />&nbsp;&nbsp;&nbsp;&nbsp;tensor: Elementwise a * x.
+Elementwise multiplication of tensor x by scalar a.<br /><br />Notes:<br />&nbsp;&nbsp;&nbsp;&nbsp;Modifes x in place<br /><br />Args:<br />&nbsp;&nbsp;&nbsp;&nbsp;x: A tensor.<br />&nbsp;&nbsp;&nbsp;&nbsp;a: scalar.<br /><br />Returns:<br />&nbsp;&nbsp;&nbsp;&nbsp;None
 
 
 ### tpow
 ```py
 
-def tpow(x: Union[torch.FloatTensor, torch.cuda.FloatTensor], a: float) -> Union[torch.FloatTensor, torch.cuda.FloatTensor]
+def tpow(x: numpy.ndarray, a: float) -> numpy.ndarray
 
 ```