mehdidc

Code for Keras plays catch blog post

Train

python qlearn.py

Test

1. Generate figures

mehdidc

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)

mehdidc

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)

mehdidc

"""
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:

mehdidc

"""
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.

mehdidc

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

mehdidc

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

mehdidc

"""
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)

mehdidc

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}'

mehdidc

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.