mirror of
https://gitlab.com/Luci_/arduino-photometrics.git
synced 2026-04-03 03:25:36 +02:00
154 lines
4.3 KiB
R
154 lines
4.3 KiB
R
# install.packages('randomForest')
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# install.packages(c('neuralnet','keras','tensorflow'),dependencies = T)
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library(tidyverse)
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library(ggplot2)
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library(lubridate)
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library(dplyr)
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library(randomForest)
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library(suntools)
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library(neuralnet)
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setwd("~/Documents/PlatformIO/Projects/Robot_Go_West/arduino-photometrics/exec")
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# Load
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photo <- read.csv("../data/photo_measures/merged_photo_data.csv", header=TRUE)
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# Time type changes
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# photo$time <- as.POSIXct(photo$Epoch)
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photo <- photo %>%
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mutate(
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datetime = as.POSIXct(Epoch, origin = "1970-01-01", tz = "Europe/Paris"),
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jour = as.Date(datetime),
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num_jour = as.numeric(format(datetime, "%j")),
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alterative_num_jour =yday(datetime),
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sin_day = sin(alterative_num_jour * (2*pi/365)),
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decimal_hour = hour(datetime) + minute(datetime)/60 + second(datetime)/3600,
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rad_hour = decimal_hour * (2*pi / 24),
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sin_hour = sin(rad_hour),
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cos_hour = cos(rad_hour)
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)
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# Same but normalised values are square root to highlight little light variations
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max_val_sensor = 254
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photo <- photo %>%
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mutate(across(starts_with("Photo_sensor"), ~ {
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.x <- sqrt(.x)
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.x <- (.x*-1) + max_val_sensor
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.x <- as.numeric(scale(.x, center = TRUE, scale = TRUE))
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}))
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# Remove NaN colomne (i had some NaN after the application of scale at a columne entirely composed of the same value)
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photo <- photo %>%
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select(where(~ !all(is.na(.x))))
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# retreive solar pos
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unix_time <- as.numeric(photo$datetime)
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lat <- 44.7912
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lon <- -0.6078
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coords <- matrix(c(lon, lat), nrow = 1)
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positions <- solarpos(coords, photo$datetime)
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df_soleil <- data.frame(
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timestamp = photo$datetime,
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utime = unix_time,
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azimut = positions[, 1],
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elevation = positions[, 2]
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)
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# Transform data to improve learning during the training phase
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df_soleil$sin_azimut <- sin(df_soleil$azimut)
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binded <- bind_cols(df_soleil, photo)
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remove(df_soleil)
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# Check elapsed time
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binded$gap_time <- abs(binded$utime - binded$Epoch)
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# Random split train and test dataset
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set.seed(123)
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binded <- binded %>% mutate(id = row_number())
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random_train_data <- binded %>% sample_frac(0.80)
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random_test_data <- anti_join(binded, random_train_data, by = "id")
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random_train_data$id <- NULL
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random_test_data$id <- NULL
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summary(random_train_data$azimut)
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summary(random_test_data$azimut)
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# Chrono split train and test dataset
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# Dataset already chrono sorted
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seuil <- floor(0.80 * nrow((binded)))
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chrono_train_data <- binded[1:seuil, ]
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chrono_test_data <- binded[(seuil + 1):nrow(binded), ]
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summary(chrono_train_data$azimut)
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summary(chrono_test_data$azimut)
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# Model creation
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nb_tree = 100
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random_model <- randomForest(
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x = random_train_data[, c("sin_day", "sin_hour", "cos_hour", "Photo_sensor0", "Photo_sensor1", "Photo_sensor2", "Photo_sensor4", "Photo_sensor5", "Temp_sensor0")],
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y = random_train_data$azimut,
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ntree = nb_tree
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)
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chrono_model <- randomForest(
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x = chrono_train_data[, c("sin_day", "sin_hour", "cos_hour", "Photo_sensor0", "Photo_sensor1", "Photo_sensor2", "Photo_sensor4", "Photo_sensor5", "Temp_sensor0")],
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y = chrono_train_data$azimut,
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ntree = nb_tree
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)
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test_random_predictions <- predict(random_model, newdata = random_test_data)
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test_chrono_predictions <- predict(chrono_model, newdata = chrono_test_data)
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test_random_results <- random_test_data
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test_chrono_results <- chrono_test_data
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test_chrono_results$predicted_azimut <- test_chrono_predictions
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test_random_results$predicted_azimut <- test_random_predictions
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head(test_random_results[, c("azimut", "predicted_azimut")])
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head(test_chrono_results[, c("azimut", "predicted_azimut")])
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comparison_chrono <- test_chrono_results[, c("azimut", "predicted_azimut", "timestamp", "decimal_hour")]
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# comparison_chrono$list = abs(comparison_chrono$azimut - comparison_chrono$predicted_azimut)
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# print(length(comparison_chrono$list))
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# print(as.integer(sum(comparison_chrono$list)))
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# mean_before_filter <- as.integer(sum(comparison_chrono$list)) / length(comparison_chrono$list)
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#
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comparison_chrono <- comparison_chrono %>%
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filter(decimal_hour > 7.0) %>%
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filter(decimal_hour < 20.0)
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comparison_chrono$comp <- abs(comparison_chrono$azimut - comparison_chrono$predicted_azimut)
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mean <- sum(comparison_chrono$comp) / length(comparison_chrono$comp)
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print(typeof(comparison_chrono))
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print(random_model)
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print(chrono_model)
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