ranjiGT

import numpy as np
X = np.array([[-0.5, 1], [-1, -1.5], [-1.5, 1.5], [1.5, -0.5], [0.5, -0.5]])
y = np.array([-1, 1, 1, 1, -1])
res = np.empty((5, 5))
def k(X):
    for i in range(X.shape[0]):
        for j in range(X.shape[0]):
            res[i][j] = np.dot(X[i], X[j]) ** 2
    return res
print(k(X))

ranjiGT

roll_calls_imp <- 
  un_roll_call_issues %>% 
  left_join(un_roll_calls) %>% 
  filter(importantvote == 1)

roll_calls_imp %>%
  count(issue) %>% 
  ggplot(aes(reorder(issue, n) , n)) + 
  geom_col() +
  coord_flip()+

ranjiGT

library(tidyverse)
category <- c("red", "blue", "green", "yellow")
tag <- c("yes", "no", "maybe", "idk")
value <- c(21, 1, 10, 9) 
df <- data.frame(category, tag, value) %>% 
   mutate(prop = value/sum(value)) 

df %>% 
  ggplot(aes(tag, prop, fill = category))+
  geom_col()+

ranjiGT

my_gapminder <- gapminder %>%
  # filter observations were the country is either Africa, Asia or Europe
  filter(continent %in% c("Africa", "Asia", "Europe")) %>%
  # compute mean country population per continent and year
  group_by(continent, year) %>%
  summarize(mean_pop = mean(pop)) %>%
  ungroup() %>%
  # compute population growth as relative difference to continent population
  # in 1958
  group_by(continent) %>%

ranjiGT

library(caret)
# Create training and test sets
set.seed(123)
trainIndex <- sample(c(FALSE,TRUE), size = nrow(dat), prob = c(.25,.75), replace = TRUE)
train_set <- dat[trainIndex, ]
test_set <- dat[!trainIndex, ]

# Learn KNN classifier
fit <- knn3Train(train_set %>% select(-y), test_set %>% select(-y), cl = train_set$y,
                 k = 3, prob = F)