From dc1e5dc869832c45047ff081efb7cf7b18e201ca Mon Sep 17 00:00:00 2001
From: mariakesa <maria.kesa@gmail.com>
Date: Mon, 10 Jun 2019 04:16:59 +0300
Subject: [PATCH] Add two parallel implementations of EP with numpy and pytorch

---
 EnsemblePursuitNumpy.py   | 20 ++++++++++++++++++++
 EnsemblePursuitPyTorch.py | 28 ++++++++++++++++++++++++++++
 2 files changed, 48 insertions(+)
 create mode 100644 EnsemblePursuitNumpy.py
 create mode 100644 EnsemblePursuitPyTorch.py

diff --git a/EnsemblePursuitNumpy.py b/EnsemblePursuitNumpy.py
new file mode 100644
index 0000000..205a4df
--- /dev/null
+++ b/EnsemblePursuitNumpy.py
@@ -0,0 +1,20 @@
+import numpy as np
+
+
+class EnsemblePursuitNumpy():
+    def __init__(self,n_ensembles,lambd,options_dict):
+        self.n_ensembles=n_ensembles
+        self.lambd=lambd
+        self.options_dict=options_dict
+
+        
+    def zscore(self,X):
+        mean_stimuli=np.mean(X,axis=1)[...,np.newaxis]
+        std_stimuli=np.std(X,axis=1)[...,np.newaxis]+0.0000000001
+        X=np.subtract(X,mean_stimuli)
+        X=np.divide(X,std_stimuli)
+        return X
+   
+    def fit_transform(self,X):
+        X=self.zscore(X)
+        print(X)
diff --git a/EnsemblePursuitPyTorch.py b/EnsemblePursuitPyTorch.py
new file mode 100644
index 0000000..d3c6423
--- /dev/null
+++ b/EnsemblePursuitPyTorch.py
@@ -0,0 +1,28 @@
+import torch
+
+class EnsemblePursuitPyTorch():
+    def __init__(self, n_ensembles, lambd, options_dict):
+        self.n_ensembles=n_ensembles
+        self.lambd=lambd
+        self.options_dict=options_dict
+    
+    def zscore(self,X):
+        #Have to transpose X to make torch.sub and div work. Transpose back into 
+        #original shape when done with calculations. 
+        mean_stimuli=X.t().mean(dim=0)
+        std_stimuli=X.t().std(dim=0)+0.0000000001
+        
+        X=torch.sub(X.t(),mean_stimuli)
+        X=X.div(std_stimuli)
+        return X.t()
+
+    def fit_transform(self,X):
+        '''
+             X-- shape (neurons, timepoints)
+        '''
+        X=torch.cuda.FloatTensor(X)
+        X=self.zscore(X)
+        print(X)
+        
+
+