Adam Levin adamwlev

## NaturalCubicSplines.ipynb

      
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                adamwlev
                / NaturalCubicSplines.ipynb
            
            
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              January 7, 2020 20:37
            
              
                Natural Cubic Splines
              
          
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## affine_transformation.py
import numpy as np

def produce_transformation(tuples,tuples_t):
	"""
	Given points in some 2d space, and points in a transformed
	2d space, produces function to tranform one space to other.

	tuples : points in original space (list of x,y tuples)
	tuples_t : corresponding points in transformed space (")
	"""

## .block
license: gpl-3.0

## monitor.py

## Makes a monitor where the mean of last x oob improvements
## are used to determine early stopping. This can be ammended
## to any stopping criteria one sees as fit - consecutive x
## negatives, more negatives than positives in last x, etc.
def make_monitor(running_mean_len):
    def monitor(i,self,args):
        if np.mean(self.oob_improvement_[max(0,i-running_mean_len+1):i+1])<0:
            return True
        else:

## TheRightAndWrongWaytoDoCrossValidation.ipynb

      
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                adamwlev
                / TheRightAndWrongWaytoDoCrossValidation.ipynb
            
            
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              September 22, 2016 19:35
            
              
                Adapted from *Elements of Statistical Learning* Friedman, Tibshirani, and Hastie
              
          
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	import numpy as np

	def produce_transformation(tuples,tuples_t):
	"""
	Given points in some 2d space, and points in a transformed
	2d space, produces function to tranform one space to other.

	tuples : points in original space (list of x,y tuples)
	tuples_t : corresponding points in transformed space (")
	"""

	## Makes a monitor where the mean of last x oob improvements
	## are used to determine early stopping. This can be ammended
	## to any stopping criteria one sees as fit - consecutive x
	## negatives, more negatives than positives in last x, etc.
	def make_monitor(running_mean_len):
	def monitor(i,self,args):
	if np.mean(self.oob_improvement_[max(0,i-running_mean_len+1):i+1])<0:
	return True
	else: