bellbind

# heap queue simple implementation

def heappush(heap, val):
    cur = len(heap)
    heap.append(val)
    while cur > 0:
        parent = (cur - 1) // 2
        if heap[parent] <= heap[cur]: break
        heap[cur], heap[parent] = heap[parent], heap[cur]
        cur = parent

jackiekazil

How do I round to 2 decimals?

In python, you have floats and decimals that can be rounded. If you care about the accuracy of rounding, use decimal type. If you use floats, you will have issues with accuracy.

All the examples use demical types, except for the original value, which is automatically casted as a float.

To set the context of what we are working with, let's start with an original value.

Original Value

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

karpathy

""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward

karpathy

"""
A bare bones examples of optimizing a black-box function (f) using
Natural Evolution Strategies (NES), where the parameter distribution is a 
gaussian of fixed standard deviation.
"""

import numpy as np
np.random.seed(0)

# the function we want to optimize

ihsgnef

import numpy as np
import matplotlib
import matplotlib.pyplot as plt

def colorize(words, color_array):
    # words is a list of words
    # color_array is an array of numbers between 0 and 1 of length equal to words
    cmap = matplotlib.cm.get_cmap('RdBu')
    template = '<span class="barcode"; style="color: black; background-color: {}">{}</span>'
    colored_string = ''

davidnvq

# Using two packages `tmux` (Ctrl+B) and `screen` (Ctrl+A):

#1. Create a new session: 
#tmux
tmux new -s session_name

#screen
screen -S session_name

#2. Attach to the session:

jeasinema

#!/usr/bin/env python
# -*- coding:UTF-8 -*-

import torch
import torch.nn as nn
import torch.nn.init as init


def weight_init(m):
    '''

williamFalcon

import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn import functional as F

"""
Blog post:
Taming LSTMs: Variable-sized mini-batches and why PyTorch is good for your health:
https://medium.com/@_willfalcon/taming-lstms-variable-sized-mini-batches-and-why-pytorch-is-good-for-your-health-61d35642972e
"""

davidnvq

import torch
from torch import nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence


class DynamicRNN(nn.Module):
	"""
	The wrapper version of recurrent modules including RNN, LSTM
	that support packed sequence batch.
	"""