As configured in my dotfiles.
start new:
tmux
start new with session name:
As configured in my dotfiles.
start new:
tmux
start new with session name:
import numpy as np | |
from scipy import linalg | |
from sklearn.utils import array2d, as_float_array | |
from sklearn.base import TransformerMixin, BaseEstimator | |
class ZCA(BaseEstimator, TransformerMixin): | |
def __init__(self, regularization=10**-5, copy=False): | |
self.regularization = regularization |
""" | |
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) |
This is unmaintained, please visit Ben-PH/spacemacs-cheatsheet
SPC q q
- quitSPC w /
- split window verticallySPC w
- - split window horizontallySPC 1
- switch to window 1SPC 2
- switch to window 2SPC w c
- delete current windowimport tensorflow as tf | |
import numpy as np | |
class TextCNN(object): | |
""" | |
A CNN for text classification. | |
Uses an embedding layer, followed by a convolutional, max-pooling and softmax layer. | |
""" | |
def __init__( |
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from sklearn.base import BaseEstimator | |
from sklearn.exceptions import NotFittedError | |
import numpy as np | |
class IRFF(BaseEstimator): | |
''' | |
Random fourier features using the improved embedding | |
https://www.cs.cmu.edu/~schneide/DougalRandomFeatures_UAI2015.pdf | |
''' |
using namespace System.Management.Automation | |
Register-ArgumentCompleter -CommandName ssh,scp,sftp -Native -ScriptBlock { | |
param($wordToComplete, $commandAst, $cursorPosition) | |
$knownHosts = Get-Content ${Env:HOMEPATH}\.ssh\known_hosts ` | |
| ForEach-Object { ([string]$_).Split(' ')[0] } ` | |
| ForEach-Object { $_.Split(',') } ` | |
| Sort-Object -Unique | |
# For now just assume it's a hostname. |