From 29f0ea17320ff6a9d9880c758aa45034270d636f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?S=C3=B6ren=20Henning?= <soeren.henning@email.uni-kiel.de>
Date: Sat, 17 Apr 2021 16:39:33 +0200
Subject: [PATCH] Correct column indices in lag trend computation
---
slope-evaluator/app/trend_slope_computer.py | 9 ++-------
1 file changed, 2 insertions(+), 7 deletions(-)
diff --git a/slope-evaluator/app/trend_slope_computer.py b/slope-evaluator/app/trend_slope_computer.py
index 631a1217d..51b28f2ba 100644
--- a/slope-evaluator/app/trend_slope_computer.py
+++ b/slope-evaluator/app/trend_slope_computer.py
@@ -4,15 +4,10 @@ import os
def compute(data, warmup_sec):
data['sec_start'] = data.loc[0:, 'timestamp'] - data.iloc[0]['timestamp']
- print(data)
regress = data.loc[data['sec_start'] >= warmup_sec] # Warm-Up
- print(regress)
-
- X = regress.iloc[:, 2].values.reshape(-1, 1) # values converts it into a numpy array
- print(X)
- Y = regress.iloc[:, 3].values.reshape(-1, 1) # -1 means that calculate the dimension of rows, but have 1 column
- print(Y)
+ X = regress.iloc[:, 1].values.reshape(-1, 1) # values converts it into a numpy array
+ Y = regress.iloc[:, 2].values.reshape(-1, 1) # -1 means that calculate the dimension of rows, but have 1 column
linear_regressor = LinearRegression() # create object for the class
linear_regressor.fit(X, Y) # perform linear regression
Y_pred = linear_regressor.predict(X) # make predictions
--
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