This script automatically sets up my vim environment on any machine. For example in a docker container or an EC2 instance.
- wget
- vim
- git
import numpy as np | |
import gym | |
env = gym.make('FetchReach-v0') | |
# Simply wrap the goal-based environment using FlattenDictWrapper | |
# and specify the keys that you would like to use. | |
env = gym.wrappers.FlattenDictWrapper( | |
env, dict_keys=['observation', 'desired_goal']) |
import numpy as np | |
import gym | |
env = gym.make('FetchReach-v0') | |
obs = env.reset() | |
done = False | |
def policy(observation, desired_goal): | |
# Here you would implement your smarter policy. In this case, |
from keras import backend as K | |
actor = None # the following code assumes that actor and critic are Graph networks | |
critic = None | |
action_input_name = 'input_action' | |
output_name = 'output' | |
batch_size = 64 | |
# Temporarily connect to a large, combined model so that we can compute the gradient and monitor | |
# the performance of the actor as evaluated by the critic. |
I hereby claim:
To claim this, I am signing this object:
@interface UIView (MPAdditions) | |
@end | |
@implementation UIView (MPAdditions) | |
- (id)debugQuickLookObject { | |
if (self.bounds.size.width < 0.0f || self.bounds.size.height < 0.0f) { | |
return nil; | |
} | |
import java.io.File; | |
import java.io.IOException; | |
public final class Test { | |
private Test() { | |
} | |
public static void main(String[] args) { | |
try { |
#!/bin/sh | |
java -classpath your/path/to/checkstyle/checkstyle-5.5/checkstyle-5.5-all.jar com.puppycrawl.tools.checkstyle.Main -c your/path/to/checkstyle_swt1.xml -r src/ |
menge = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] | |
menge.each do |x| | |
if x != 0 then | |
menge.each do |y| | |
if y != 0 then | |
if ((x * y) % menge.length == 0) then | |
print x.to_s + " * " + y.to_s + " % " + menge.length.to_s + " = 0\n" | |
end | |
end | |
end |