From 8eac3d5af1cc49e76e3087be747300e2e740e319 Mon Sep 17 00:00:00 2001
From: art <learthurgo@gmail.com>
Date: Tue, 8 Oct 2019 13:49:19 -0500
Subject: [PATCH] ok fixed half of it

---
 data analysis/analysis/analysis.py | 38 ++++++++++++++----------------
 1 file changed, 18 insertions(+), 20 deletions(-)

diff --git a/data analysis/analysis/analysis.py b/data analysis/analysis/analysis.py
index db6c764d..78591b44 100644
--- a/data analysis/analysis/analysis.py	
+++ b/data analysis/analysis/analysis.py	
@@ -429,24 +429,6 @@ class Regression:
 
     #todo: document completely
 
-    def factorial(n):
-        if n==0:
-            return 1
-        else:
-            return n*factorial(n-1)
-    def num_poly_terms(num_vars, power):
-        if power == 0:
-            return 0
-        return int(factorial(num_vars+power-1) / factorial(power) / factorial(num_vars-1)) + num_poly_terms(num_vars, power-1)
-
-    def take_all_pwrs(vec,pwr):
-        #todo: vectorize (kinda)
-        combins=torch.combinations(vec, r=pwr, with_replacement=True)
-        out=torch.ones(combins.size()[0])
-        for i in torch.t(combins):
-            out *= i
-        return torch.cat(out,take_all_pwrs(vec, pwr-1))
-
     def set_device(new_device):
         global device
         device=new_device
@@ -535,15 +517,31 @@ class Regression:
         power=None
         def __init__(self, num_vars, power):
             self.power=power
-            num_terms=num_poly_terms(num_vars, power)
+            num_terms=self.num_poly_terms(num_vars, power)
             self.weights=torch.rand(num_terms, requires_grad=True, device=device)
             self.bias=torch.rand(1, requires_grad=True, device=device)
             self.parameters=[self.weights,self.bias]
+        def num_poly_terms(self,num_vars, power):
+            if power == 0:
+                return 0
+            return int(self.factorial(num_vars+power-1) / self.factorial(power) / self.factorial(num_vars-1)) + self.num_poly_terms(num_vars, power-1)
+        def factorial(self,n):
+            if n==0:
+                return 1
+            else:
+                return n*self.factorial(n-1)
+        def take_all_pwrs(self, vec,pwr):
+            #todo: vectorize (kinda)
+            combins=torch.combinations(vec, r=pwr, with_replacement=True)
+            out=torch.ones(combins.size()[0])
+            for i in torch.t(combins):
+                out *= i
+            return torch.cat(out,take_all_pwrs(vec, pwr-1))
         def forward(self,mtx):
             #TODO: Vectorize the last part
             cols=[]
             for i in torch.t(mtx):
-                cols.append(take_all_pwrs(i,self.power))
+                cols.append(self.take_all_pwrs(i,self.power))
             new_mtx=torch.t(torch.stack(cols))
             long_bias=self.bias.repeat([1,mtx.size()[1]])
             return torch.matmul(self.weights,new_mtx)+long_bias