doug ybarbo alexland

## package-name-expander.R

pkg_name = paste("~/path/to/package", system('cd /path/to/package; ls adStats*', intern=T), sep="/")

install.packages(pkg_name, repos=NULL, type="source")


## sort_tapply.R

DF1 = DF[order(DF$factor_var, -xtfrm(DF$numeric_var)),]
DF1$idx = sequence(rle(as.character(DF1$factor_var))$lengths)


$> # e.g., sort a data frame based on 'price' (numeric variable)
$> # 'grouped by' the levels of 'color' (factor, or discrete variable)

$> data(diamonds, package="ggplot2")

## commify.R
print_fn = function(x) {print(format(x, big.mark=','), quote=F)}

## exception_class_id.py

try:
    ...
except Exception, e:
	print("caught an exception defined in class {0}".format(e.__class__.__module__))

## flatten.py
# first, a compact one-liner but limited to 2D sequences (nested just one level):

# obvious, but
	# "nested_list" refers to the nested list you want to flatten,
	# "row" refers to each list comprising the nested_list
	# "itm" refers to each element comprising each row


def flatten(list2D):
	return [itm for row in list2d for itm in row]

## python idioms
# useful eg, for recursive functions such as merge sort or binary tree building/traversal in which sometimes the fn must return one of two portions of some container (a sequence, tree node, etc.)

def fnx(a):
	if a % 2 == 0:
		x = 45
		y = None
	else:
		x = None
		y = 3
	return (x or y) + 2

## column-print.py
# let's say you have four sequences you want to print columnwise, like so:

19       59       97       44
92       57       63       68
66       21       69       90
75       66       12       19

# mock some data
import random as RND
gen_row = lambda: [ RND.randint(10, 99) for c in range(3) ]

## factorial.py
#Newbie programmer
def factorial(x):
    if x == 0:
        return 1
    else:
        return x * factorial(x - 1)
print factorial(6)


#First year programmer, studied Pascal

## NLP-category-predict-ML-1.ipynb

      
              1 file
            
          
              0 forks
            
          
              0 comments
            
          
              0 stars
            
          
                alexland
                / NLP-category-predict-ML-1.ipynb
            
            
              Created
              May 30, 2014 21:41
            
          
      Sorry, something went wrong. Reload?
      Sorry, we cannot display this file.
      Sorry, this file is invalid so it cannot be displayed.
      
          Viewer requires iframe.
      
    
## softmax-mlp.py
'''
these 2 functions assume this sort of MLP architecture:
  (i) a classification problem modeled w/ softmax activation function
  (ii) encode the output layer w/ 1-of-N
    > so for raw data like this:
        .3, .2, .6, .1, 'class I'
        .6, .1, .8, .4, 'class II'
        .5, .2, .7, .3, 'class III'

      recode it for intput to a softmax MLP like so:

	pkg_name = paste("~/path/to/package", system('cd /path/to/package; ls adStats*', intern=T), sep="/")

	install.packages(pkg_name, repos=NULL, type="source")

	DF1 = DF[order(DF$factor_var, -xtfrm(DF$numeric_var)),]
	DF1$idx = sequence(rle(as.character(DF1$factor_var))$lengths)


	$> # e.g., sort a data frame based on 'price' (numeric variable)
	$> # 'grouped by' the levels of 'color' (factor, or discrete variable)

	$> data(diamonds, package="ggplot2")

	try:
	...
	except Exception, e:
	print("caught an exception defined in class {0}".format(e.__class__.__module__))
	# first, a compact one-liner but limited to 2D sequences (nested just one level):

	# obvious, but
	# "nested_list" refers to the nested list you want to flatten,
	# "row" refers to each list comprising the nested_list
	# "itm" refers to each element comprising each row


	def flatten(list2D):
	return [itm for row in list2d for itm in row]
	# useful eg, for recursive functions such as merge sort or binary tree building/traversal in which sometimes the fn must return one of two portions of some container (a sequence, tree node, etc.)

	def fnx(a):
	if a % 2 == 0:
	x = 45
	y = None
	else:
	x = None
	y = 3
	return (x or y) + 2
	# let's say you have four sequences you want to print columnwise, like so:

	19 59 97 44
	92 57 63 68
	66 21 69 90
	75 66 12 19

	# mock some data
	import random as RND
	gen_row = lambda: [ RND.randint(10, 99) for c in range(3) ]
	#Newbie programmer
	def factorial(x):
	if x == 0:
	return 1
	else:
	return x * factorial(x - 1)
	print factorial(6)


	#First year programmer, studied Pascal
	'''
	these 2 functions assume this sort of MLP architecture:
	(i) a classification problem modeled w/ softmax activation function
	(ii) encode the output layer w/ 1-of-N
	> so for raw data like this:
	.3, .2, .6, .1, 'class I'
	.6, .1, .8, .4, 'class II'
	.5, .2, .7, .3, 'class III'

	recode it for intput to a softmax MLP like so: