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nateGeorge / rfr_test.py
Created May 5, 2020
Demonstrate sklearn RandomForestRegressor strange behavior
View rfr_test.py
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)
View keybase.md

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
nateGeorge / timeseries_cnn.py
Created May 19, 2017 — forked from jkleint/timeseries_cnn.py
Example of using Keras to implement a 1D convolutional neural network (CNN) for timeseries prediction.
View timeseries_cnn.py
#!/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
nateGeorge / readme.md
Created Aug 15, 2016 — forked from baraldilorenzo/readme.md
VGG-16 pre-trained model for Keras
View readme.md

##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
nateGeorge / coding_standard.py
Created Jul 16, 2016
coding standards, originally from enthought
View coding_standard.py
# 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.