Scott Sievert stsievert

## fftconv-conv-timings-2d.ipynb

      
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                stsievert
                / fftconv-conv-timings-2d.ipynb
            
            
              Created
              July 24, 2019 16:39
            
              
                constant timing for convolution methods (fft and direct)
              
          
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## timing.ipynb

      
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                stsievert
                / timing.ipynb
            
            
              Created
              April 15, 2019 13:34
            
              
                Tornado yielding list
              
          
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## redirect-choice.html
<head>
<meta http-equiv="content-type" content="text/html; charset=utf-8" />
<meta http-equiv="expires" content="0">
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1">

<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css">
<script src="https://ajax.googleapis.com/ajax/libs/jquery/2.1.3/jquery.min.js"></script>
<script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/js/bootstrap.min.js"></script>
</head>

## test_scipy_convolve.py
import numpy as np
from scipy.signal import convolve

def test(type1, type2):
    np.random.seed(42)
    n = 3000
    if 'int' in type1 or 'bool' in type1:
        x1 = np.random.choice([0, 1], size=n).astype(type1)
    else:
        x1 = np.random.randn(n).astype(type1)

## inc-search.py
from dask_ml.model_selection import RandomizedSearchCV
from dask_ml.wrappers import Incremental
from dask_ml.datasets import make_classification
from sklearn.linear_model import SGDClassifier
import numpy as np
from sklearn.model_selection import KFold

X, y = make_classification(chunks=20)

params = {'estimator__alpha': np.logspace(-3, 0)}

## Noise-model.ipynb

      
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                / Noise-model.ipynb
            
            
              Last active
              October 24, 2018 20:21
            
              
                For the strange fruit triplets
              
          
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## _Hyperband-example-test-patience.ipynb

      
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                / _Hyperband-example-test-patience.ipynb
            
            
              Last active
              September 10, 2018 18:52
            
              
                Testing patience for hyperparam search
              
          
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## _Hyperband-example.ipynb

      
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              1 star
            
          
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                / _Hyperband-example.ipynb
            
            
              Last active
              April 14, 2019 23:12
            
              
                Hyperparameter comparisons (with successive halving, hyperband, stop on plateau and passive random sampling)
              
          
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## a-incremental-model-selection.ipynb

      
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                stsievert
                / a-incremental-model-selection.ipynb
            
            
              Created
              August 22, 2018 23:31
            
              
                Successive halving and "stop on plateau" comparison
              
          
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## PyTorch-autoencoder.ipynb

      
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                / PyTorch-autoencoder.ipynb
            
            
              Last active
              February 14, 2022 21:20
            
              
                PyTorch MNIST autoencoder
              
          
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	<head>
	<meta http-equiv="content-type" content="text/html; charset=utf-8" />
	<meta http-equiv="expires" content="0">
	<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
	<meta http-equiv="X-UA-Compatible" content="IE=edge,chrome=1">

	<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/css/bootstrap.min.css">
	<script src="https://ajax.googleapis.com/ajax/libs/jquery/2.1.3/jquery.min.js"></script>
	<script src="https://maxcdn.bootstrapcdn.com/bootstrap/3.3.6/js/bootstrap.min.js"></script>
	</head>
	import numpy as np
	from scipy.signal import convolve

	def test(type1, type2):
	np.random.seed(42)
	n = 3000
	if 'int' in type1 or 'bool' in type1:
	x1 = np.random.choice([0, 1], size=n).astype(type1)
	else:
	x1 = np.random.randn(n).astype(type1)
	from dask_ml.model_selection import RandomizedSearchCV
	from dask_ml.wrappers import Incremental
	from dask_ml.datasets import make_classification
	from sklearn.linear_model import SGDClassifier
	import numpy as np
	from sklearn.model_selection import KFold

	X, y = make_classification(chunks=20)

	params = {'estimator__alpha': np.logspace(-3, 0)}