glemaitre

python examples/model_selection/grid_search_text_feature_extraction.py 

==========================================================
Sample pipeline for text feature extraction and evaluation
==========================================================

The dataset used in this example is the 20 newsgroups dataset which will be
automatically downloaded and then cached and reused for the document
classification example.

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Test script

from __future__ import division, print_function                                                 
                                                                                                
import platform                                                                                 
import sys                                                                                      
                                                                                                
from time import time                                                                           
                                                                                                

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import numpy as np

from sklearn.preprocessing import QuantileTransformer

X = np.array([0] * 1 + [0.5] * 7 + [1] * 2).reshape(-1, 1)

qt = QuantileTransformer(n_quantiles=10)
qt.fit(X)

# a behaviour which is not desired, but that frankly should

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"""
This is real case using the data of the Adult Census dataset available at:
https://archive.ics.uci.edu/ml/datasets/Adult

It will show that adding a smoothing noise do not has any influence on the
classification performance but allow for a better understanding when manually
checking the QuantileTransformer.
"""
import numpy as np
import pandas as pd

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PRs

Need Development

#8684 #8607 #8835 #8350 #8217

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from skcycling.data_management import Rider                                                                          
                                                                                                                     
filename = '../data/rider/user_5.p'                                                                                  
my_rider = Rider.load_from_pickles(filename)                                                                         
print('This rider has {} rides.'.format(len(my_rider.rides_pp_)))

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def rep_boitier_inertiel(Ax, Ay, Az, q0, q1, q2, q3):
    """TODO: Docstring for rep_boitier_inertiel.
    :Ax: Acc lin x
    :Ay: Acc lin y
    :Az: Acc lin z
    :q0: quat q0
    :q1: quat q1
    :q2: quat q2
    :q3: quat q3
    :returns: array Ax, Ay, Az corrigé dans l'espace

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#cython: cdivision=True
#cython: boundscheck=False
#cython: nonecheck=False
#cython: wraparound=False

from libc.stdlib cimport malloc, free, realloc

import numpy as np

from ..transform import integral_image

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from abc import ABCMeta, abstractmethod
from random import randint

import six


class BaseChiffrement(six.with_metaclass(ABCMeta)):

    @staticmethod
    def _check_input(texte, cle):

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# --- ploting the original angles ---#
fig, xxx = plt.subplots(nrows=3, ncols=2, figsize=(18, 10), sharex=True)

((ax0, ax1), (ax2, ax3), (ax4, ax5)) = xxx

ax0.plot(imu_eG[:, 0], '-', c='k', linewidth=3, label='GT', alpha=0.8)
ax0.plot(pre_angle[:, 0], ':', lw=2,
         label=r'No noise$= {:1.3f} \pm {:1.3f}$'.format(0.087, 0.078))
ax0.plot(pre_angle[:, 0] + np.random.random(pre_angle[:, 0].shape) - 0.5, '-.', lw=2,
         label=r'Noisy$= {:1.3f} \pm {:1.3f}$'.format(0.087, 0.078))