Code for Keras plays catch blog post
python qlearn.py
- Generate figures
Code for Keras plays catch blog post
python qlearn.py
import numpy as np | |
import matplotlib as mpl | |
mpl.use('Agg') | |
import matplotlib.pyplot as plt | |
from pyearth import Earth | |
import time | |
from itertools import product | |
np.random.seed(2) |
import numpy as np | |
import os | |
import matplotlib as mpl | |
mpl.use('Agg') | |
import matplotlib.pyplot as plt | |
from pyearth import Earth | |
import time | |
np.random.seed(2) |
""" | |
This is an implementation of the distance proposed by "An Information Geometry of Statistical Manifold Learning, Ke Sun, Stéphane Marchand-Maillet | |
" to measure quality of embedding techniques (e.g PCA, Isomap, LLE, TSNE). It is needs data points matrix of | |
the original data as well as the same data points with the embedding coordinates, and some additional parameters to fix. | |
How to use ? | |
============ | |
Basic swiss roll example: |
""" | |
Implementation of 'Maximum Likelihood Estimation of Intrinsic Dimension' by Elizaveta Levina and Peter J. Bickel | |
how to use | |
---------- | |
The goal is to estimate intrinsic dimensionality of data, the estimation of dimensionality is scale dependent | |
(depending on how much you zoom into the data distribution you can find different dimesionality), so they | |
propose to average it over different scales, the interval of the scales [k1, k2] are the only parameters of the algorithm. |
import numpy | |
from scipy.ndimage.interpolation import map_coordinates | |
from scipy.ndimage.filters import gaussian_filter | |
def elastic_transform(image, alpha, sigma, random_state=None): | |
"""Elastic deformation of images as described in [Simard2003]_. | |
.. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for | |
Convolutional Neural Networks applied to Visual Document Analysis", in |
import numpy as np | |
import time | |
from joblib import Memory | |
import pandas as pd | |
from bokeh.charts import show, Bar | |
from bokeh.io import output_file, vplot |
""" | |
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) |
from tempfile import mkdtemp | |
import shutil | |
import os | |
import sys | |
import subprocess | |
from skimage.io import imsave | |
cmd_tpl = 'ffmpeg -y -framerate {framerate} -i {pattern} -c:v libx264 -r {rate} -pix_fmt yuv420p {out}' |
from sklearn.ensemble import RandomForestRegressor | |
from sklearn.base import clone | |
import numpy as np | |
class GAM(object): | |
""" | |
Simple generalized additive model which fits each feature w.r.t | |
the output independently using a model specified by | |
base_estimator. the final predictions can be done by regressing linearly | |
the predictions of the model of each features by using a pipeline. |