Andreas Mueller amueller

## colors_curves.png

      
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              3 stars
            
          
                amueller
                / colors_curves.png
            
            
              Last active
              April 20, 2024 01:38
            
              
                random color palette using halton codes
              
          
## gist:1351047
from sklearn.datasets import load_digits
from sklearn.svm import SVC
from sklearn.utils import shuffle
from sklearn.metrics import zero_one_score
import numpy as np


digits = load_digits()
X, y = shuffle(digits.data, digits.target)
X_train, X_test = X[:1000, :], X[1000:, :]

## filterbank.py
##########################################################################
# Maximum Response filterbank from
# http://www.robots.ox.ac.uk/~vgg/research/texclass/filters.html
# based on several edge and bar filters.
# Adapted to Python by Andreas Mueller amueller@ais.uni-bonn.de
# Share and enjoy
#

import numpy as np
import matplotlib.pyplot as plt

## Diagram1.dia

      
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                amueller
                / Diagram1.dia
            
            
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              August 18, 2022 05:03
            
              
                Machine learning cheat sheet diagram svg and dia file
              
          
            View raw
        
    
## dpgmm_sampler.py
import numpy as np
import scipy.stats


class ChineseRestaurantProcess(object):
    def __init__(self, alpha):
        self.alpha = alpha
        self.customers = []
    def sample(self, n_samples=1):
        samples = []

## digits_video.py
# (c) 2012 Andreas Mueller amueller@ais.uni-bonn.de
# License: BSD 2-Clause
#
# See my blog for details: http://peekaboo-vision.blogspot.com

import numpy as np

import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation

## block-coroutine.ipynb

      
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                amueller
                / block-coroutine.ipynb
            
            
              Created
              February 25, 2021 23:18
                — forked from minrk/block-coroutine.ipynb
            
          
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## mnist_kernel_approx.py
# Standard scientific Python imports
import pylab as pl
import numpy as np
from time import time

# Import datasets, classifiers and performance metrics
from sklearn import datasets, svm, pipeline
from sklearn.kernel_approximation import (RBFSampler,
                                          Nystroem)
from sklearn.utils import shuffle

## abomination.py
from sklearn.base import BaseEstimator

def piper(self, other):
    from sklearn.pipeline import make_pipeline, Pipeline
    if isinstance(self, Pipeline):
        steps = ([estimator for (name, estimator) in self.steps] + [other])
        return make_pipeline(*steps)
    else:
        return make_pipeline(self, other)

## mlp.py
import numpy as np
import warnings

from itertools import cycle, izip

from sklearn.utils import gen_even_slices
from sklearn.utils import shuffle
from sklearn.base import BaseEstimator
from sklearn.base import ClassifierMixin
from sklearn.preprocessing import LabelBinarizer
	from sklearn.datasets import load_digits
	from sklearn.svm import SVC
	from sklearn.utils import shuffle
	from sklearn.metrics import zero_one_score
	import numpy as np


	digits = load_digits()
	X, y = shuffle(digits.data, digits.target)
	X_train, X_test = X[:1000, :], X[1000:, :]
	##########################################################################
	# Maximum Response filterbank from
	# http://www.robots.ox.ac.uk/~vgg/research/texclass/filters.html
	# based on several edge and bar filters.
	# Adapted to Python by Andreas Mueller amueller@ais.uni-bonn.de
	# Share and enjoy
	#

	import numpy as np
	import matplotlib.pyplot as plt
	import numpy as np
	import scipy.stats


	class ChineseRestaurantProcess(object):
	def __init__(self, alpha):
	self.alpha = alpha
	self.customers = []
	def sample(self, n_samples=1):
	samples = []
	# (c) 2012 Andreas Mueller amueller@ais.uni-bonn.de
	# License: BSD 2-Clause
	#
	# See my blog for details: http://peekaboo-vision.blogspot.com

	import numpy as np

	import matplotlib.pyplot as plt
	from matplotlib.animation import FuncAnimation
	# Standard scientific Python imports
	import pylab as pl
	import numpy as np
	from time import time

	# Import datasets, classifiers and performance metrics
	from sklearn import datasets, svm, pipeline
	from sklearn.kernel_approximation import (RBFSampler,
	Nystroem)
	from sklearn.utils import shuffle
	from sklearn.base import BaseEstimator

	def piper(self, other):
	from sklearn.pipeline import make_pipeline, Pipeline
	if isinstance(self, Pipeline):
	steps = ([estimator for (name, estimator) in self.steps] + [other])
	return make_pipeline(*steps)
	else:
	return make_pipeline(self, other)
	import numpy as np
	import warnings

	from itertools import cycle, izip

	from sklearn.utils import gen_even_slices
	from sklearn.utils import shuffle
	from sklearn.base import BaseEstimator
	from sklearn.base import ClassifierMixin
	from sklearn.preprocessing import LabelBinarizer