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 |