nateGeorge

import numpy as np
from sklearn.ensemble import RandomForestRegressor

# simulate data
# 12 rows train, 6 rows test, 5 features, 3 columns for target
features = np.random.random((12, 5))
targets = np.random.random((12, 3))
test_features = np.random.random((6, 5))

rfr = RandomForestRegressor(random_state=42)

nateGeorge

Keybase proof

I hereby claim:

• I am nategeorge on github.
• I am ngeorge (https://keybase.io/ngeorge) on keybase.
• I have a public key ASBN62tlMiBh7pp7XiZdcynW33evbt0WQ_b5c9JwWV6H1Qo

To claim this, I am signing this object:

nateGeorge

#!/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

nateGeorge

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

nateGeorge

# taken from here: http://web.archive.org/web/20110527163743/https://svn.enthought.com/enthought/browser/sandbox/docs/coding_standard.py

""" This module is an example of the Enthought Python coding standards.

It was adapted from the Python Enhancement Proposal 8 (aka PEP 8) titled
'Style Guide for Python Code' (http://www.python.org/peps/pep-0008.html).

The first item in a module must be a documentation string (docstring).  The
first line of the docstring should be a one line summary.  If a more
detailed description is required, put an empty line before it.