#' @inheritParams getZeroDeltaForecast
getNeighborsDeltaForecast = function(data, today, memory, horizon, shape_params, ...)
{
- if (any(is.na(shape_params$weights) | is.na(shape_params$indices)))
+ first_day = max(1, today-memory)
+ filter = shape_params$indices >= first_day
+ indices = shape_params$indices[filter]
+ weights = shape_params$weights[filter]
+ if (any(is.na(weights) | is.na(indices)))
return (NA)
- gaps = sapply(shape_params$indices, function(i) {
+ gaps = sapply(indices, function(i) {
data$getSerie(i+1)[1] - tail(data$getSerie(i), 1)
})
- scal_product = shape_params$weights * gaps
- norm_fact = sum( shape_params$weights[!is.na(scal_product)] )
+ scal_product = weights * gaps
+ norm_fact = sum( weights[!is.na(scal_product)] )
sum(scal_product, na.rm=TRUE) / norm_fact
}
#' @inheritParams getZeroDeltaForecast
getPersistenceDeltaForecast = function(data, today, memory, horizon, shape_params, ...)
{
- # Return level of last similar "tomorrow" (thus -6) computed on "horizon"
- data$getSerie(today-6)[1] - tail(data$getSerie(today-7), 1)
+ #return gap between end of similar day curve and first day of tomorrow (in the past)
+ first_day = max(1, today-memory)
+ index = today-7
+ repeat
+ {
+ {
+ last_similar_serie_end = tail( data$getCenteredSerie(index), 1)
+ last_similar_tomorrow_begin = data$getCenteredSerie(index+1)[1]
+ index = index - 7
+ };
+ if (!is.na(last_similar_serie_end) && !is.na(last_similar_tomorrow_begin))
+ return (last_similar_tomorrow_begin - last_similar_serie_end);
+ if (index < first_day)
+ return (NA)
+ }
}
{
"Get time values at specified index"
+ index = dateIndexToInteger(index, .self)
data[[index]]$time
},
getCenteredSerie = function(index)
{
"Get serie values (centered) at specified index"
+ index = dateIndexToInteger(index, .self)
data[[index]]$serie
},
getLevel = function(index)
{
"Get level for the serie at specified index"
+ index = dateIndexToInteger(index, .self)
data[[index]]$level
},
getSerie = function(index)
{
"Get serie values (centered+level) at specified index"
+ index = dateIndexToInteger(index, .self)
data[[index]]$serie + data[[index]]$level
},
getExoHat = function(index)
{
"Get exogeous predictions at specified index"
+ index = dateIndexToInteger(index, .self)
data[[index]]$exo_hat
},
getExoDm1 = function(index)
{
"Get exogenous measures the day before specified index"
+ index = dateIndexToInteger(index, .self)
data[[index]]$exo_Dm1
}
)
},
getIndexInData = function(index)
{
- "Get (day)index at specified index"
+ "Get (day) index in data where prediction took place"
pred[[index]]$index
}
#'
#' @title Average Shape Forecaster
#'
-#' @description Return the (pointwise) average of the all the centered day curves in the past.
-#' Inherits \code{\link{ShapeForecaster}}
+#' @description Return the (pointwise) average of the all the (similar) centered day curves
+#' in the past. Inherits \code{\link{ShapeForecaster}}
AverageShapeForecaster = setRefClass(
Class = "AverageShapeForecaster",
contains = "ShapeForecaster",
predict = function(today, memory, horizon, ...)
{
avg = rep(0., horizon)
- for (i in (today-memory):today)
+ first_day = max(1, today-memory)
+ index = today-7 + 1
+ nb_no_na_series = 0
+ repeat
{
- if (!any(is.na(data$getSerie(i))))
- avg = avg + data$getCenteredSerie(i)
+ {
+ serie_on_horizon = data$getCenteredSerie(index)[1:horizon]
+ index = index - 7
+ };
+ if (!any(is.na(serie_on_horizon)))
+ {
+ avg = avg + serie_on_horizon
+ nb_no_na_series = nb_no_na_series + 1
+ };
+ if (index < first_day)
+ break
}
- avg
+ avg / nb_no_na_series
}
)
)
if (length(data$getCenteredSerie(1)) < length(data$getCenteredSerie(2)))
first_day = 2
- # Predict only on non-NAs days (TODO:)
- if (any(is.na(data$getSerie(today))))
- return (NA)
+ # Predict only on (almost) non-NAs days
+ nas_in_serie = is.na(data$getSerie(today))
+ if (any(nas_in_serie))
+ {
+ #TODO: better define "repairing" conditions (and method)
+ if (sum(nas_in_serie) >= length(nas_in_serie) / 2)
+ return (NA)
+ for (i in seq_along(nas_in_serie))
+ {
+ if (nas_in_serie[i])
+ {
+ #look left
+ left = i-1
+ while (left>=1 && nas_in_serie[left])
+ left = left-1
+ #look right
+ right = i+1
+ while (right<=length(nas_in_serie) && nas_in_serie[right])
+ right = right+1
+ #HACK: modify by-reference Data object...
+ data$data[[today]]$serie[i] <<-
+ if (left==0) data$data[[today]]$serie[right]
+ else if (right==0) data$data[[today]]$serie[left]
+ else (data$data[[today]]$serie[left] + data$data[[today]]$serie[right]) / 2.
+ }
+ }
+ }
# Determine indices of no-NAs days followed by no-NAs tomorrows
fdays_indices = c()
#'
#' @title Persistence Shape Forecaster
#'
-#' @description Return the last centered last (similar) day curve (may be a lot of NAs,
-#' but we cannot do better). Inherits \code{\link{ShapeForecaster}}
+#' @description Return the last centered last (similar) day curve.
+#' Inherits \code{\link{ShapeForecaster}}
PersistenceShapeForecaster = setRefClass(
Class = "PersistenceShapeForecaster",
contains = "ShapeForecaster",
},
predict = function(today, memory, horizon, ...)
{
- #return centered last (similar) day curve (may be a lot of NAs, but we cannot do better)
- last_similar_serie = data$getCenteredSerie(today-6)[1:horizon]
- if (any(is.na(last_similar_serie))) #TODO:
- return (NA)
- last_similar_serie
+ #return centered last (similar) day curve, avoiding NAs until memory is run
+ first_day = max(1, today-memory)
+ index = today-7 + 1
+ repeat
+ {
+ {
+ last_similar_serie = data$getCenteredSerie(index)[1:horizon]
+ index = index - 7
+ };
+ if (!any(is.na(last_similar_serie)))
+ return (last_similar_serie);
+ if (index < first_day)
+ return (NA)
+ }
}
)
)
#' @param date_format How date/time are stored (e.g. year/month/day hour:minutes;
#' see \code{strptime})
#' @param working_tz Timezone to work with ("GMT" or e.g. "Europe/Paris")
-#' @param predict_at When does the prediction take place ? ab[:cd][:ef] where a,b,c,d,e,f
-#' in (0,9) and define an hour[minute[second]]; time must be present in the file
+#' @param predict_at When does the prediction take place ? Integer, in hours. Default: 0
#'
#' @return An object of class Data
#'
#' @export
getData = function(ts_data, exo_data,
- input_tz="GMT", date_format="%d/%m/%Y %H:%M", working_tz="GMT", predict_at="00")
+ input_tz="GMT", date_format="%d/%m/%Y %H:%M", working_tz="GMT", predict_at=0)
{
# Sanity checks (not full, but sufficient at this stage)
if (!is.character(input_tz) || !is.character(working_tz))
stop("Bad time-series input (data frame or CSV file)")
if (is.character(ts_data))
ts_data = ts_data[1]
- pattern_index_in_predict_at = grep("^[0-9]{2}(:[0-9]{2}){0,2}$", predict_at)
- if (!is.character(predict_at) || length(pattern_index_in_predict_at) == 0)
- stop("Bad predict_at ( ^[0-9]{2}(:[0-9]{2}){0,2}$ )")
- predict_at = predict_at[ pattern_index_in_predict_at[1] ]
+ predict_at = as.integer(predict_at)[1]
+ if (predict_at<0 || predict_at>23)
+ stop("Bad predict_at (0-23)")
if (!is.character(date_format))
stop("Bad date_format (character)")
date_format = date_format[1]
formatted_dates_POSIXlt = strptime(as.character(ts_df[,1]), date_format, tz=input_tz)
ts_df[,1] = format(as.POSIXct(formatted_dates_POSIXlt), tz=working_tz, usetz=TRUE)
- if (nchar(predict_at) == 2)
- predict_at = paste(predict_at,":00",sep="")
- if (nchar(predict_at) == 5)
- predict_at = paste(predict_at,":00",sep="")
-
line = 1 #index in PM10 file (24 lines for 1 cell)
nb_lines = nrow(ts_df)
nb_exos = ( ncol(exo_df) - 1 ) / 2
{
time = c()
serie = c()
- repeat {
- {
- time = c(time, ts_df[line,1])
- serie = c(serie, ts_df[line,2])
- line = line + 1
- };
- if (line >= nb_lines + 1
- # NOTE: always second part of date/time, because it has been formatted
- || strsplit(as.character(ts_df[line-1,1])," ")[[1]][2] == predict_at)
+ repeat
{
- break
- }}
+ {
+ time = c(time, ts_df[line,1])
+ serie = c(serie, ts_df[line,2])
+ line = line + 1
+ };
+ if (line >= nb_lines + 1 || as.POSIXlt(ts_df[line-1,1])$hour == predict_at)
+ break
+ }
# NOTE: if predict_at does not cut days at midnight,
# for the exogenous to be synchronized they need to be shifted
- if (predict_at != "00:00:00")
+ if (predict_at > 0)
{
exo_hat = as.data.frame(exo_df[max(1,i-1),(1+nb_exos+1):(1+2*nb_exos)])
exo_Dm1 = if (i>=3) as.data.frame(exo_df[i-1,2:(1+nb_exos)]) else NA
horizon = as.integer(horizon)[1]
if (horizon<=0 || horizon>length(data$getCenteredSerie(2)))
stop("Horizon too short or too long")
- indices = as.integer(indices)
+ indices = sapply( seq_along(indices), function(i) dateIndexToInteger(indices[i], data) )
if (any(indices<=0 | indices>data$getSize()))
stop("Indices out of range")
indices = sapply(indices, dateIndexToInteger, data)
pred = list()
for (today in indices)
{
- #NOTE: To estimate timing...
-# print(paste("Predict until index",today))
-
#shape always predicted first (on centered series, no scaling taken into account),
#with side-effect: optimize some parameters (h, weights, ...)
predicted_shape = shape_forecaster$predict(today, memory, horizon, ...)
#TODO: this way is faster than a call to append(); why ?
pred[[length(pred)+1]] = list(
# Predict shape and align it on end of current day
- serie = predicted_shape + tail( data$getSerie(today), 1 ) - predicted_shape[1],
+ serie = predicted_shape + tail( data$getSerie(today), 1 ) - predicted_shape[1] +
predicted_delta, #add predicted jump
params = shape_forecaster$getParameters(),
index = today )
par(mar=c(4.7,5,1,1), cex.axis=2, cex.lab=2, lwd=2)
measure = data$getSerie(pred$getIndexInData(index)+1)[1:horizon]
yrange = range( pred$getSerie(index), measure )
- plot(measure, type="l", ylim=yrange, lwd=3)
+ plot(measure, type="l", ylim=yrange, lwd=3, xlab="Temps (en heures)", ylab="PM10")
par(new=TRUE)
- plot(pred$getSerie(index), type="l", col=2, ylim=yrange, lwd=3)
+ plot(pred$getSerie(index), type="l", col="#0000FF", ylim=yrange, lwd=3, xlab="", ylab="")
}
#' @title Plot filaments
#' @description Draw error graphs, potentially from several runs of \code{getForecast}
#'
#' @param err Error as returned by \code{getError}
+#' @param cols Colors for each error (default: 1,2,3,...)
#'
#' @seealso \code{\link{plotPredReal}}, \code{\link{plotFilaments}}, \code{\link{plotSimils}}
#' \code{\link{plotFbox}}
#'
#' @export
-plotError <- function(err)
+plotError <- function(err, cols=seq_along(err))
{
+ if (!is.null(err$abs))
+ err = list(err)
par(mfrow=c(2,2), mar=c(4.7,5,1,1), cex.axis=2, cex.lab=2, lwd=2)
L = length(err)
yrange = range( sapply(1:L, function(index) ( err[[index]]$abs$day ) ), na.rm=TRUE )
for (i in seq_len(L))
{
plot(err[[i]]$abs$day, type="l", xlab=ifelse(i==1,"Temps (heures)",""),
- ylab=ifelse(i==1,"Moyenne |y - y_hat|",""), ylim=yrange, col=i)
+ ylab=ifelse(i==1,"Moyenne |y - y_hat|",""), ylim=yrange, col=cols[i])
if (i < L)
par(new=TRUE)
}
for (i in seq_len(L))
{
plot(err[[i]]$abs$indices, type="l", xlab=ifelse(i==1,"Temps (jours)",""),
- ylab=ifelse(i==1,"Moyenne |y - y_hat|",""), ylim=yrange, col=i)
+ ylab=ifelse(i==1,"Moyenne |y - y_hat|",""), ylim=yrange, col=cols[i])
if (i < L)
par(new=TRUE)
}
for (i in seq_len(L))
{
plot(err[[i]]$MAPE$day, type="l", xlab=ifelse(i==1,"Temps (heures)",""),
- ylab=ifelse(i==1,"MAPE moyen",""), ylim=yrange, col=i)
+ ylab=ifelse(i==1,"MAPE moyen",""), ylim=yrange, col=cols[i])
if (i < L)
par(new=TRUE)
}
for (i in seq_len(L))
{
plot(err[[i]]$MAPE$indices, type="l", xlab=ifelse(i==1,"Temps (jours)",""),
- ylab=ifelse(i==1,"MAPE moyen",""), ylim=yrange, col=i)
+ ylab=ifelse(i==1,"MAPE moyen",""), ylim=yrange, col=cols[i])
if (i < L)
par(new=TRUE)
}
#' @param fiter Optional filter: return TRUE on indices to process
#'
#' @export
-plotFbox <- function(data, filter=function(index) (TRUE))
+plotFbox <- function(data, filter=function(index) TRUE)
{
if (!requireNamespace("rainbow", quietly=TRUE))
stop("Functional boxplot requires the rainbow package")
#' @export
dateIndexToInteger = function(index, data)
{
+ index = index[1]
if (is.numeric(index))
index = as.integer(index)
- #Undocumented "private" method to translate index --> date (is needed)
if (is.integer(index))
- return (index[1])
- if (is.character(index))
+ return (index)
+ if (inherits(index, "Date") || is.character(index))
{
- tryCatch(dt <- as.POSIXct(index[1]), finally=print("Unrecognized index format"))
+ tryCatch(dt <- as.POSIXct(index), error=function(e) stop("Unrecognized index format"))
#TODO: tz arg to difftime ?
- integerIndex <- as.integer( difftime(dt, data$getTime(1)) ) + 1
+ integerIndex <- round( (as.numeric( difftime(dt, data$getTime(1)) ))[1] ) + 1
if (integerIndex > 0 && integerIndex <= data$getSize())
- return (integerIndex)
+ {
+ #WARNING: if series start at date >0h, result must be shifted
+ date1 = as.POSIXlt(data$getTime(1)[1])
+ date2 = as.POSIXlt(data$getTime(2)[1])
+ shift = (date1$year==date2$year && date1$mon==date2$mon && date1$mday==date2$mday)
+ return (integerIndex + ifelse(shift,1,0))
+ }
stop("Date outside data range")
}
stop("Unrecognized index format")
}
+#' @title integerIndexToDate
+#'
+#' @description Transform an integer index to date index (relative to data)
+#'
+#' @param index Date (or integer) index
+#' @param data Object of class \code{Data}
+#'
+#' @export
+integerIndexToDate = function(index, data)
+{
+ index = index[1]
+ if (is.numeric(index))
+ index = as.integer(index)
+ if (!is.integer(index))
+ stop("'index' should be an integer")
+ as.Date( data$getTime(index)[1] )
+}
+
#' @title getSimilarDaysIndices
#'
#' @description Find similar days indices in the past
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {
"collapsed": false
},
- "outputs": [
- {
- "ename": "ERROR",
- "evalue": "Error in eval(expr, envir, enclos): could not find function \"getData\"\n",
- "output_type": "error",
- "traceback": [
- "Error in eval(expr, envir, enclos): could not find function \"getData\"\nTraceback:\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# Voir ?getData pour les arguments\n",
"data = getData(ts_data=\"data/pm10_mesures_H_loc.csv\", exo_data=\"data/meteo_extra_noNAs.csv\",\n",
--- /dev/null
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "library(talweg)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "data = getData(ts_data=\"../data/pm10_mesures_H_loc.csv\", exo_data=\"../data/meteo_extra_noNAs.csv\",\n",
+ " input_tz = \"Europe/Paris\", working_tz=\"Europe/Paris\", predict_at=7)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Pollution par chauffage"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "p_ch_nn = getForecast(data, seq(as.Date(\"2015-01-18\"),as.Date(\"2015-01-24\"),\"days\"), Inf, 17,\n",
+ " \"Neighbors\", \"Neighbors\", simtype=\"mix\", same_season=FALSE, mix_strategy=\"mult\")\n",
+ "p_ch_pz = getForecast(data, seq(as.Date(\"2015-01-18\"),as.Date(\"2015-01-24\"),\"days\"), Inf, 17,\n",
+ " \"Persistence\", \"Zero\")\n",
+ "p_ch_az = getForecast(data, seq(as.Date(\"2015-01-18\"),as.Date(\"2015-01-24\"),\"days\"), Inf, 17,\n",
+ " \"Average\", \"Zero\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "e_ch_nn = getError(data, p_ch_nn, 17)\n",
+ "e_ch_pz = getError(data, p_ch_pz, 17)\n",
+ "e_ch_az = getError(data, p_ch_az, 17)\n",
+ "options(repr.plot.width=9, repr.plot.height=6)\n",
+ "plotError(list(e_ch_nn, e_ch_pz, e_ch_az), cols=c(1,2,colors()[258]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "par(mfrow=c(1,2))\n",
+ "options(repr.plot.width=9, repr.plot.height=4)\n",
+ "plotPredReal(data, p_ch_nn, 3)\n",
+ "plotPredReal(data, p_ch_nn, 4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "par(mfrow=c(1,2))\n",
+ "plotFilaments(data, p_ch_nn$getIndexInData(3))\n",
+ "plotFilaments(data, p_ch_nn$getIndexInData(4))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "par(mfrow=c(1,3))\n",
+ "plotSimils(p_ch_nn, 3)\n",
+ "plotSimils(p_ch_nn, 4)\n",
+ "plotSimils(p_ch_nn, 5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Pollution par épandage"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "p_ep_nn = getForecast(data, seq(as.Date(\"2015-03-15\"),as.Date(\"2015-03-21\"),\"days\"), Inf, 17,\n",
+ " \"Neighbors\", \"Neighbors\", simtype=\"mix\", same_season=FALSE, mix_strategy=\"mult\")\n",
+ "p_ep_pz = getForecast(data, seq(as.Date(\"2015-03-15\"),as.Date(\"2015-03-21\"),\"days\"), Inf, 17,\n",
+ " \"Persistence\", \"Zero\")\n",
+ "p_ep_az = getForecast(data, seq(as.Date(\"2015-03-15\"),as.Date(\"2015-03-21\"),\"days\"), Inf, 17,\n",
+ " \"Average\", \"Zero\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "e_ep_nn = getError(data, p_ep_nn, 17)\n",
+ "e_ep_pz = getError(data, p_ep_pz, 17)\n",
+ "e_ep_az = getError(data, p_ep_az, 17)\n",
+ "options(repr.plot.width=9, repr.plot.height=6)\n",
+ "plotError(list(e_ep_nn, e_ep_pz, e_ep_az), cols=c(1,2,colors()[258]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "par(mfrow=c(1,2))\n",
+ "options(repr.plot.width=9, repr.plot.height=4)\n",
+ "plotPredReal(data, p_ep_nn, 3)\n",
+ "plotPredReal(data, p_ep_nn, 4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "par(mfrow=c(1,2))\n",
+ "plotFilaments(data, p_ep_nn$getIndexInData(3))\n",
+ "plotFilaments(data, p_ep_nn$getIndexInData(4))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "par(mfrow=c(1,3))\n",
+ "plotSimils(p_ep_nn, 3)\n",
+ "plotSimils(p_ep_nn, 4)\n",
+ "plotSimils(p_ep_nn, 5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Semaine non polluée"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "p_np_nn = getForecast(data, seq(as.Date(\"2015-04-26\"),as.Date(\"2015-05-02\"),\"days\"), Inf, 17,\n",
+ " \"Neighbors\", \"Neighbors\", simtype=\"mix\", same_season=FALSE, mix_strategy=\"mult\")\n",
+ "p_np_pz = getForecast(data, seq(as.Date(\"2015-04-26\"),as.Date(\"2015-05-02\"),\"days\"), Inf, 17,\n",
+ " \"Persistence\", \"Zero\")\n",
+ "p_np_az = getForecast(data, seq(as.Date(\"2015-04-26\"),as.Date(\"2015-05-02\"),\"days\"), Inf, 17,\n",
+ " \"Average\", \"Zero\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "e_np_nn = getError(data, p_np_nn, 17)\n",
+ "e_np_pz = getError(data, p_np_pz, 17)\n",
+ "e_np_az = getError(data, p_np_az, 17)\n",
+ "options(repr.plot.width=9, repr.plot.height=6)\n",
+ "plotError(list(e_np_nn, e_np_pz, e_np_az), cols=c(1,2,colors()[258]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "par(mfrow=c(1,2))\n",
+ "options(repr.plot.width=9, repr.plot.height=4)\n",
+ "plotPredReal(data, p_np_nn, 3)\n",
+ "plotPredReal(data, p_np_nn, 4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "par(mfrow=c(1,2))\n",
+ "plotFilaments(data, p_np_nn$getIndexInData(3))\n",
+ "plotFilaments(data, p_np_nn$getIndexInData(4))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "par(mfrow=c(1,3))\n",
+ "plotSimils(p_np_nn, 3)\n",
+ "plotSimils(p_np_nn, 4)\n",
+ "plotSimils(p_np_nn, 5)"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "R",
+ "language": "R",
+ "name": "ir"
+ },
+ "language_info": {
+ "codemirror_mode": "r",
+ "file_extension": ".r",
+ "mimetype": "text/x-r-source",
+ "name": "R",
+ "pygments_lexer": "r",
+ "version": "3.3.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
projects / talweg.git / commitdiff