cadrev

cadrev

#!/bin/python
'''
Usage:
python downsample.py [offset+]amount

Examples:
cat super_big.csv | python downsample.py 1+4 > big_divided_by_4.csv
cat data.csv | python downsample.py 2 > data_halved.csv
'''

cadrev

# Create an array of Multinomial Naive Bayes
multi_class = [MultinomialNB(alpha=factor) for factor in np.concatenate((np.arange(0, 3.1, 0.1), [5, 10]))]

for nb in multi_class:
    nb.fit(X_train, y_train)
    
    
    
import seaborn as sns
from sklearn.metrics import precision_recall_curve

cadrev

#!/usr/bin/env python
"""
Example of using Keras to implement a 1D convolutional neural network (CNN) for timeseries prediction.
"""

from __future__ import print_function, division

import numpy as np
from keras.layers import Convolution1D, Dense, MaxPooling1D, Flatten
from keras.models import Sequential

cadrev

# R code to produce a simulated dataset for an experiment on a made up insect. 
# Measures include sex, body length, thorax width, number of thoracic bristles and some measure of aggression behaviour. 
# Also there is exposure to some treatment stimulus/drug. 
# This simulation uses Copulas to generate correlated variables from binomial, Gaussian and Poisson distributions

require(copula)

set.seed(1888)

n <- 1000 

cadrev

""" 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

cadrev

Libraries for a modern geospatial workflow

Distribution

• Writing, Running, and Distributing
• Tox driven development
• Publishing python libraries

Numpy

cadrev

#How to download streaming video

Streaming just means a download that they don't want you to keep.  But Chrome's developer tools make it easy to access what's really going on under the hood.

##Open Developer Tools

From the page where you want to download some things, go into your chrome menu to open the developer tools.  You can either:

	1.  (On a mac): Command-option-J
	2.  (On a PC): Control-alt-J

cadrev

import numpy as np

from keras.datasets import imdb
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.layers import containers
from keras.layers.noise import GaussianNoise
from keras.layers.core import Dense, AutoEncoder
from keras.utils import np_utils
from sklearn.metrics import (precision_score, recall_score,

cadrev

Programming

1. Line
2. 2
3. fafas