Nikolay Mayorov nmayorov

## hyp1f1_decimal.py
from decimal import Decimal, getcontext
import numpy as np
from math import isfinite


EPS = np.finfo(float).eps


class ComplexDecimal(object):
    def __init__(self, real, imag):

## fs_communities_and_crimes.py
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sklearn.preprocessing import Imputer
from sklearn.feature_selection import (
    SelectKBest, MutualInfoSelector, f_regression)
from sklearn.linear_model import RidgeCV
from sklearn.model_selection import cross_val_score

data = pd.read_csv('communities.data', header=None)

## feature_selection_rfe.py
import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import load_digits
from sklearn.feature_selection import RFE, MutualInfoSelector
from sklearn.preprocessing import minmax_scale
from sklearn.svm import LinearSVC
from sklearn.model_selection import cross_val_score

digits = load_digits()
X = minmax_scale(digits.data)

## feature_selection_rf.py
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_diabetes, load_boston
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import cross_val_score
from sklearn.feature_selection import (SelectKBest, MutualInfoSelector,
                                       f_classif, f_regression)
from sklearn.pipeline import make_pipeline


## discrete_bvp.ipynb

      
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                nmayorov
                / discrete_bvp.ipynb
            
            
              Last active
              November 30, 2015 16:34
            
              
                Solving a discrete boundary-value problem in scipy
              
          
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## feature_selection_pipeline.py
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import (load_digits, load_breast_cancer,
                              load_diabetes, load_boston)
from sklearn.linear_model import RidgeCV
from sklearn.preprocessing import minmax_scale
from sklearn.model_selection import cross_val_score
from sklearn.feature_selection import (SelectKBest, MutualInfoSelector,
                                       f_classif, f_regression)
from sklearn.svm import LinearSVC

## feature_selection_comparison.py
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import (load_digits, load_breast_cancer,
                              load_diabetes, load_boston)
from sklearn.linear_model import RidgeCV
from sklearn.preprocessing import minmax_scale
from sklearn.model_selection import cross_val_score
from sklearn.feature_selection import (SelectKBest, MutualInfoSelector,
                                       f_classif, f_regression)
from sklearn.svm import LinearSVC

## robust_regression.ipynb

      
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              5 stars
            
          
                nmayorov
                / robust_regression.ipynb
            
            
              Last active
              March 2, 2022 06:46
            
              
                Robust nonlinear regression in scipy
              
          
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## bundle_adjustment_scipy.ipynb

      
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                nmayorov
                / bundle_adjustment_scipy.ipynb
            
            
              Last active
              September 19, 2021 02:52
            
              
                Large scale bundle adjustment in scipy
              
          
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## rosenbrock_leastsqbound.py
from __future__ import print_function

import numpy as np
from leastsqbound import leastsqbound


def fun(x):
    return np.array([10 * (x[1] - x[0]**2), (1 - x[0])])
	from decimal import Decimal, getcontext
	import numpy as np
	from math import isfinite


	EPS = np.finfo(float).eps


	class ComplexDecimal(object):
	def __init__(self, real, imag):
	import matplotlib.pyplot as plt
	import pandas as pd
	import numpy as np
	from sklearn.preprocessing import Imputer
	from sklearn.feature_selection import (
	SelectKBest, MutualInfoSelector, f_regression)
	from sklearn.linear_model import RidgeCV
	from sklearn.model_selection import cross_val_score

	data = pd.read_csv('communities.data', header=None)
	import matplotlib.pyplot as plt
	import numpy as np
	from sklearn.datasets import load_digits
	from sklearn.feature_selection import RFE, MutualInfoSelector
	from sklearn.preprocessing import minmax_scale
	from sklearn.svm import LinearSVC
	from sklearn.model_selection import cross_val_score

	digits = load_digits()
	X = minmax_scale(digits.data)
	import numpy as np
	import matplotlib.pyplot as plt
	from sklearn.datasets import load_diabetes, load_boston
	from sklearn.ensemble import RandomForestRegressor
	from sklearn.model_selection import cross_val_score
	from sklearn.feature_selection import (SelectKBest, MutualInfoSelector,
	f_classif, f_regression)
	from sklearn.pipeline import make_pipeline
	import numpy as np
	import matplotlib.pyplot as plt
	from sklearn.datasets import (load_digits, load_breast_cancer,
	load_diabetes, load_boston)
	from sklearn.linear_model import RidgeCV
	from sklearn.preprocessing import minmax_scale
	from sklearn.model_selection import cross_val_score
	from sklearn.feature_selection import (SelectKBest, MutualInfoSelector,
	f_classif, f_regression)
	from sklearn.svm import LinearSVC
	from __future__ import print_function

	import numpy as np
	from leastsqbound import leastsqbound


	def fun(x):
	return np.array([10 * (x[1] - x[0]**2), (1 - x[0])])