From 04ee93756f991771992e4903d1daa6bf0bd23d03 Mon Sep 17 00:00:00 2001 From: Kleanthis Koupidis Date: Fri, 2 Mar 2018 03:30:00 +0200 Subject: [PATCH] .... --- vignettes/TrafficBDE.R | 10 ++ vignettes/TrafficBDE.html | 347 ++++++-------------------------------- 2 files changed, 58 insertions(+), 299 deletions(-) create mode 100644 vignettes/TrafficBDE.R diff --git a/vignettes/TrafficBDE.R b/vignettes/TrafficBDE.R new file mode 100644 index 0000000..3eeb67f --- /dev/null +++ b/vignettes/TrafficBDE.R @@ -0,0 +1,10 @@ +## ---- warning=FALSE------------------------------------------------------ +library(TrafficBDE) +Data <- X163204843_1 + +## ---- include=TRUE,eval=FALSE, warning=FALSE----------------------------- +# kStepsForward(Data = Data, Link_id = "163204843", direction = "1", datetime = "2017-01-27 14:00:00", predict = "Mean_speed", steps = 1) + +## ---- eval=FALSE, include=TRUE, message=FALSE, warning=FALSE------------- +# kStepsForward(Data = Data, Link_id = "163204843", direction = "1", datetime = "2017-01-15 20:00:00", predict = "Entries", steps = 1) + diff --git a/vignettes/TrafficBDE.html b/vignettes/TrafficBDE.html index 7eacff3..a0a1c3a 100644 --- a/vignettes/TrafficBDE.html +++ b/vignettes/TrafficBDE.html @@ -8,125 +8,72 @@ + -TrafficBDE +Getting started with TrafficBDE + - - - - - - - - - - - - + - - - - -
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Intoduction

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Introduction

This package was created in order to enable the creation of a neural network model, for the needs of a European project. “TrafficBDE” includes functions for properly formulating the data, training the neural network and predicted the wanted variable. This document introduces you to TrafficBDE’s basic set of tools.

The user should use only the loadData and the kStepsForward functions. The first one to load the historical data and the second for the computation of the predicted value.

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Output

Examples

Simple examples the kStepsForward function are provided, in order for the user to understand the use and how to deal with these function.

The sample of the dataset that is being used is available in TrafficBDE package and represents the traffic fload of the road with Link_id: “163204843”, for January 2017.

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The first example provides, in one step, the prediction of the Mean speed at 14.00 on 27 Jan. 2017

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library(TrafficBDE)
-Data <- loadData("D:/packages/okgreece/TrafficBDE/data/163204843_1.RData")
-
-kStepsForward(Data = Data, Link_id = "163204843", direction = "1", datetime = "2017-01-27 14:00:00", predict = "Mean_speed", steps = 1)
-
## Training...
-
## Loading required package: lattice
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## Loading required package: ggplot2
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## + Fold01: layer1=4, layer2=3, layer3=4 
-## - Fold01: layer1=4, layer2=3, layer3=4 
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-## - Fold10: layer1=5, layer2=4, layer3=4 
-## Aggregating results
-## Selecting tuning parameters
-## Fitting layer1 = 4, layer2 = 4, layer3 = 4 on full training set
-## Training Completed.
-## 
-## Time taken for training:  2.599840037
-## Predicting Mean_speed for the Next Quarter...
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##                       Predicted Real Value        RMSE
-## 2017-01-27 14:00:00 39.36150787         29 10.36150787
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The second example provides, in one step, the prediction of the Entries at 20.00 on 15 Jan. 2017

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## Training...
-## + Fold01: layer1=4, layer2=3, layer3=4 
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-## Aggregating results
-## Selecting tuning parameters
-## Fitting layer1 = 4, layer2 = 3, layer3 = 4 on full training set
-## Training Completed.
-## 
-## Time taken for training:  2.073361166
-## Predicting Entries for the Next Quarter...
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##                       Predicted Real Value          RMSE
-## 2017-01-15 20:00:00 1.012088733          1 0.01208873324
+

The first example provides, in one step, the prediction of the Mean speed at 14:00 on 27 Jan. 2017

+
library(TrafficBDE)
+Data <- X163204843_1
+
kStepsForward(Data = Data, Link_id = "163204843", direction = "1", datetime = "2017-01-27 14:00:00", predict = "Mean_speed", steps = 1)
+

The second example provides, in one step, the prediction of the Entries at 20:00 on 15 Jan. 2017

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kStepsForward(Data = Data, Link_id = "163204843", direction = "1", datetime = "2017-01-15 20:00:00", predict = "Entries", steps = 1)
- - - - -