sillasgonzaga

# Criando um Correlation Plot
def visualize_correlation_matrix(data, hurdle = 0.0):
  # fonte: data science academy
    R = np.corrcoef(data, rowvar = 0)
    R[np.where(np.abs(R) < hurdle)] = 0.0
    heatmap = plt.pcolor(R, cmap = mpl.cm.coolwarm, alpha = 0.8)
    heatmap.axes.set_frame_on(False)
    heatmap.axes.set_yticks(np.arange(R.shape[0]) + 0.5, minor = False)
    heatmap.axes.set_xticks(np.arange(R.shape[1]) + 0.5, minor = False)
    heatmap.axes.set_xticklabels(variables, minor = False)

sillasgonzaga

library(BatchGetSymbols)
library(tidyverse)
library(varngc)

x <- GetSP500Stocks()

stocks <- c("AAPL", "GOOG", "MSFT", "AMZN", "ADBE", "EBAY", "FB", "HPQ")

st <- as.Date("2010-01-01")
end <- as.Date("2018-01-01")

sillasgonzaga

# devtools::install_github("timelyportfolio/sunburstR")
library(tidyverse)
library(sunburstR)

sequences <- read.csv(
  system.file("examples/visit-sequences.csv",package="sunburstR")
  ,header = FALSE
  ,stringsAsFactors = FALSE
)[1:200,]

sillasgonzaga

---
title: "Aula 3: Gráficos no R"
output:
  html_notebook:
    css: custom.css
    number_sections: yes
    toc: yes
    toc_float: true
---

sillasgonzaga

library(tidyverse)
library(fs)

dir_path <- "projetos/site-master/content/blog/"

dir_info(dir_posts, recursive = TRUE) %>% 
  head() %>%
  knitr::kable()

rmd_or_r_file_paths_tbl <- dir_info(dir_path, recursive = T) %>%

sillasgonzaga

# https://github.com/Selbosh/user2017

library(tidyverse)
library(rvest)
library(xml2)
library(ggraph)
library(igraph)
# https://rud.is/dl/r-bloggers-feedly-streams.rds
df <- read_rds("r-bloggers-feedly-streams.rds")

sillasgonzaga

library(tidyverse)
library(igraph)
library(ggraph)
library(abjutils)

df <- read_rds("data/socios_cvm.rds")
glimpse(df)

df_clean <- df %>% 
  filter(tipo == "02") %>% 

sillasgonzaga

nrbf <- function(X, Y, X_test = X, k = ncol(X), gamma = 1.0, seed = 123, plot = TRUE){
  library(corpcor)
  library(neuralnet)
  set.seed(seed)
  #### Definição dos argumentos:
  # X: Matriz de input 
  # Y: Matriz de output
  # k: número de centros (polos)
  # gamma: parametro de aprendizado
  

sillasgonzaga

### Módulo 2
### Matriz de adjacência
library(igraph)
library(igraphdata)

# exemplo de matriz de adjacencia
data("karate")
karate
# transformar grafo em matriz de adjacencia
igraph::as_adjacency_matrix(karate)

sillasgonzaga

---
title: "Aula 4.1 - Modelagem"
output:
  html_notebook:
    css: custom.css
    number_sections: yes
    toc: yes
    toc_float: true
---