Deepak Sadulla dsadulla

## vif_fun.r
vif_func<-function(in_frame,thresh=10,trace=T,...){

  library(fmsb)

  if(any(!'data.frame' %in% class(in_frame))) in_frame<-data.frame(in_frame)

  #get initial vif value for all comparisons of variables
  vif_init<-NULL
  var_names <- names(in_frame)
  for(val in var_names){

## LilLambda.ipynb

      
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                dsadulla
                / LilLambda.ipynb
            
            
              Created
              September 12, 2017 18:28
                — forked from vakila/LilLambda.ipynb
            
              
                Anjana Vakil, "Mary had a little lambda", EuroPython 2017: https://ep2017.europython.eu/conference/talks/mary-had-a-little-lambda
              
          
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## timeseries_cnn.py
#!/usr/bin/env python
"""
Example of using Keras to implement a 1D convolutional neural network (CNN) for timeseries prediction.
"""

from __future__ import print_function, division

import numpy as np
from keras.layers import Convolution1D, Dense, MaxPooling1D, Flatten
from keras.models import Sequential
	vif_func<-function(in_frame,thresh=10,trace=T,...){

	library(fmsb)

	if(any(!'data.frame' %in% class(in_frame))) in_frame<-data.frame(in_frame)

	#get initial vif value for all comparisons of variables
	vif_init<-NULL
	var_names <- names(in_frame)
	for(val in var_names){
	#!/usr/bin/env python
	"""
	Example of using Keras to implement a 1D convolutional neural network (CNN) for timeseries prediction.
	"""

	from __future__ import print_function, division

	import numpy as np
	from keras.layers import Convolution1D, Dense, MaxPooling1D, Flatten
	from keras.models import Sequential