From 9e76b6c16018bb8f53995226e8fc0985b6455929 Mon Sep 17 00:00:00 2001
From: Ryan Missel <rxm7244@rit.edu>
Date: Sun, 31 Mar 2019 09:38:09 -0400
Subject: [PATCH] Auto stash before merge of "master" and "origin/master"

---
 hypotheses_modeling/model.ckpt      | Bin 4443 -> 4443 bytes
 hypotheses_modeling/pytorch_shit.py |  11 +++++++++--
 2 files changed, 9 insertions(+), 2 deletions(-)

diff --git a/hypotheses_modeling/model.ckpt b/hypotheses_modeling/model.ckpt
index 7ab304026c704dccd1c109030959810b233c7987..0668d2848c24e82ddd181f6d85d0cb2347562871 100644
GIT binary patch
delta 398
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delta 398
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diff --git a/hypotheses_modeling/pytorch_shit.py b/hypotheses_modeling/pytorch_shit.py
index 483c9bc..413aeed 100644
--- a/hypotheses_modeling/pytorch_shit.py
+++ b/hypotheses_modeling/pytorch_shit.py
@@ -24,7 +24,9 @@ def get_argmax(array):
             max = array[i]
             index = i
 
-    return [index]
+    one_hot = [0,0,0,0]
+    one_hot[index] = 1
+    return one_hot
 
 
 def get_trainset(dataset, k, n0, x_columns, y_columns):
@@ -71,6 +73,9 @@ def time_series_sigmoid_classification(steps, dataset, k, n0, x_columns, y_colum
   optimizer = optim.Adam(net.parameters(), lr=.001)
   loss = nn.CrossEntropyLoss()
 
+  x, y = get_trainset(dataset, k, n0, x_columns, y_columns)
+  accuracy(net, x, y)
+
   for step in range(steps):
       optimizer.zero_grad()
 
@@ -89,6 +94,8 @@ def time_series_sigmoid_classification(steps, dataset, k, n0, x_columns, y_colum
 def accuracy(net, x, y):
     pred = net(x)
     pred = pred.detach().numpy()
+    for row in range(len(pred)):
+        pred[row] = get_argmax(pred[row])
 
     total = len(pred)
     correct = 0
@@ -110,7 +117,7 @@ def main():
   df = pd.read_csv(filename)
   x = ["day", "playerID", "fatigueSliding"]
   y = ["day", "playerID", "BestOutOfMyselfAbsolutely", "BestOutOfMyselfSomewhat", "BestOutOfMyselfNotAtAll", "BestOutOfMyselfUnknown"]
-  time_series_sigmoid_classification(100, df, 0, 30, x, y)
+  time_series_sigmoid_classification(2, df, 0, 30, x, y)
 
 
 if __name__ == '__main__':