White Kernel, which should behave like this
k(x1, x2) = 0 if x1 != x2 else noies_level
is always returning zero:
from sklearn.gaussian_process import kernels
import numpy as np
import sklearn
print(kernels.WhiteKernel(noise_level=2.0)(np.array([[1.0]]), np.array([[1.0]])))
print(sklearn.__version__)
Result:
[[0.]]
1.1.1
I'm expecting nonzero result. What am I missing here?
I want to multiply a string value to be entered from the screen as many random uniques as the number of elements in the list I have. For example, the user will enter a company name on the screen, for example NIKE. Let's say I have phone numbers on the list. How can I generate unique values such as NIKE1, NIKE2, NIKE3, NIKEW, NIKEW1 as much as the number of phone numbers(list size) for Java8?
My game variables
#Game rules
time = 1
time_passed = 0
complete_time = 400
enemy_timer = random.choice([1000,2000,3000])
finished_enemy = pygame.time.get_ticks()
enemies_alive = 0
My creation of enemies
#creating enemies
if time_passed < complete_time :
if pygame.time.get_ticks() - finished_enemy > enemy_timer:
r = random.randint(0, len(enemy_types) - 1)
enemy = Enemy(enemy_health[r],enemy_animations[r], WINDOW_WIDTH - -10, WINDOW_HEIGHT - 260, 2) #gets arguments from enemy class and where they are(location)
enemy_1 = Enemy(enemy_health[r],enemy_animations[r], WINDOW_WIDTH - -10, WINDOW_HEIGHT - 160, 1)
enemy_2 = Enemy(enemy_health[r],enemy_animations[r], WINDOW_WIDTH - -10, WINDOW_HEIGHT - 60, 3)
enemy_group.add(enemy,enemy_1,enemy_2)
finished_enemy = pygame.time.get_ticks()
#allows for increasing difficulty
time_passed += enemy_health[r]
print(time_passed)
Sorry for wording of the question as i dont know how to describe what i want in a sentence.
Anyway I have the code for randomizing three values for enemy_timer (1000,2000,3000) what it does is separate the images of my enemy by millisecond. I want it to affect enemy, enemy_1 and enemy_2 differently. Such as when i run enemy has 1000 enemy_2 has 3000 so on.
What Import Statements are needed for the following?
My variables aren't comparing to each other. Is it because it is in a class, or is there some other problem?
import random
def game():
result = random.uniform(1, 10)
get = input("I'm thinking of a number, 1-10")
if get == result:
print("GG, the number was" + result)
game()
if get != result:
print("That wasn't my number. My number was" + result + "Let's try again.")
game()
game()
so the purpose of the code is to shuffle a vector, then print a random number from the vector, and then delete that same number so it won't repeat. what I did is:
The problem is when I do songs.pop_back(); it removes the last element from the original vector and not from the shuffled one, so it makes numbers coming back that way. and I get an error.
code:
int getSong(int n, vector<int> songs, vector<int>::iterator iter) {
random_device random;
shuffle(songs.begin(), songs.end(), random);
for (int j = 0; j < n; j++) {
cout << songs[j] << " ";
}
iter = songs.end() -1 ;
int song = *iter;
iter--;
return song;
}
int main() {
vector<int> songs = { 1,2,3,4,5,6,7,8,9 };
int n = songs.size();
vector<int>::iterator iter;
for (int i = 0; i < n; n--) {
int l = getSong(n, songs, iter);
cout << "The song number is:" << l << "\n" << endl;
songs.pop_back();
}
return 0;
}
the output is: 
Thank you!
In my R code, while I generate a number with sample (1: edge_counter, 1) everything works fine but while I generate it with rbinom (1, i, p) the code doesn't work. What could be the problem?
Here is the code where i generate a graph following the Forrest Fire module, the random generation problem is when i generate a random x:
install.packages("igraph")
library(igraph)
par(mar=c(1,1,1,1))
#Number of node to add in the graph
num_nodes = 10
#Forward burning probability
p = 0.37
#Backward burning ratio
r = 0.32
#Parameters for the plot
node_count <- rep(0, times = num_nodes)
edge_count <- rep(0, times = num_nodes)
g1 <- make_empty_graph(1)
for(i in 2:num_nodes)
{
exit = 0
visited_node <- rep(FALSE, times = i)
g1 = add_vertices(g1,1)
#orphans extention
q = runif(1,0,1)
if(q > 0.2)
{
#chose an ambassador node
w <- sample(1:(vcount(g1)-1), 1)
# connecting the ambassador with the node
v = i;
g1 = add_edges(g1,c(v,w))
# count number of node linked to the ambassador
edge_counter <- 0
for(h in 1: i)
{
if(g1[w, h] == 1 || g1[h, w] == 1)
{
edge_counter = edge_counter + 1
}
}
selected_node <- rep(FALSE, times = i)
while(exit == 0)
{
x = sample(1:edge_counter, 1)
#x = rbinom(1, i, p)
# select x links incident to w choosing from among both out links and in links
j = 0
while(j != x)
{
prob <- runif(1,0,1)
random_node <- sample(1:i, 1)
if(selected_node[random_node] == FALSE)
{
#chose out-link
if(prob < r)
{
if(g1[w, random_node] == 1)
{
selected_node[random_node] = TRUE
j = j+1
}
}
#chose in-link
else
{
if (g1[random_node, w] == 1)
{
selected_node[random_node] = TRUE
j = j+1
}
}
}
}
#v forms out-links to w1,...wx
for(l in 1:i)
{
if((selected_node[l] == TRUE) && (l != v))
{
g1 = add_edges(g1,c(v,l))
}
}
old_v <- v
visited_node[v] = TRUE
for(j in 1:i)
{
if ((selected_node[j] == TRUE) && (visited_node[v] == FALSE))
{
v <- j
break
}
}
if(old_v == v)
{
exit = 1
}
}
}
#Node and edge count
node_count[i] = gorder(g1)
edge_count[i] = gsize(g1)
}
# number of nodes respect number of edges
plot(node_count, edge_count, xlab = "Number of nodes", ylab = "Number of edges")
#graph plot
plot(g1)