eecrazy

#!/usr/bin/env python3.6
"""
Outputs an .eps visualization of shuffling algorithm to stdout.
Requires Python 3.6+ (for f-strings).
"""
import numpy as np

def create(width, height):
    print(f"""%!PS-Adobe-3.0 EPSF-3.0
%%BoundingBox: 0 0 {width} {height}

eecrazy

注意：本文内容适用于 Tmux 2.3 及以上的版本，但是绝大部分的特性低版本也都适用，鼠标支持、VI 模式、插件管理在低版本可能会与本文不兼容。

Tmux 快捷键 & 速查表

启动新会话：

tmux [new -s 会话名 -n 窗口名]

恢复会话：

eecrazy

# http://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html

def exp_lr_scheduler(optimizer, epoch, init_lr=0.001, lr_decay_epoch=7):
    """Decay learning rate by a factor of 0.1 every lr_decay_epoch epochs."""
    lr = init_lr * (0.1**(epoch // lr_decay_epoch))

    if epoch % lr_decay_epoch == 0:
        print('LR is set to {}'.format(lr))

    for param_group in optimizer.param_groups:

eecrazy

import numpy as np
import gym
from gym import wrappers
from gym.spaces import Discrete, Box

# ================================================================
# Policies
# ================================================================

class DeterministicDiscreteActionLinearPolicy(object):

eecrazy

import gym
import numpy as np, pandas as pd

from sklearn.neural_network import MLPClassifier
import matplotlib.pyplot as plt


env = gym.make("MountainCar-v0")
env.reset()

eecrazy

import numpy as np
import gym
from gym import wrappers
from gym.spaces import Discrete, Box

# ================================================================
# Policies
# ================================================================

class DeterministicDiscreteActionLinearPolicy(object):

eecrazy

sudo yum install libmpc-devel mpfr-devel gmp-devel

cd ~/Downloads
curl ftp://ftp.mirrorservice.org/sites/sourceware.org/pub/gcc/releases/gcc-4.9.2/gcc-4.9.2.tar.bz2 -O
tar xvfj gcc-4.9.2.tar.bz2

cd gcc-4.9.2
./configure --disable-multilib --enable-languages=c,c++
make -j 4
make install

eecrazy

from gensim import models

sentence = models.doc2vec.LabeledSentence(
    words=[u'so`bme', u'words', u'here'], tags=["SENT_0"])
sentence1 = models.doc2vec.LabeledSentence(
    words=[u'here', u'we', u'go'], tags=["SENT_1"])

sentences = [sentence, sentence1]

class LabeledLineSentence(object):

eecrazy

Refer to https://clang-omp.github.io/ .

We need to install something:

brew install libiomp
brew install clang-omp

Then we need to set the environment:

eecrazy

#kill all processes containing run_zyli

#short cut:
pss run_zyli |cut -d " " -f 7 |xargs kill


#original:
ps -ef | grep run_zyli |cut -d " " -f 7 |xargs kill