<commit_before>	#FOR TESTING##################################################

	#OPTION 1 - 1D matrix
	#x=np.array([[1], [2], [3], [4]])
    
    #OPTION 2 - 2D matrix 
    #x=np.array([[1, 11], [2, 12], [3, 13], [4, 14]])

    #OPTION 3 - 3d matrix
    #x=np.array([[1, 11, 21] [2, 12, 22] [3, 13, 33] [4, 14, 44]])


    #OPTION 4 - use "weights" --> import weights.mat and assign to x as numpy.ndarray
	#import scipy.io 
	#data_0=scipy.io.loadmat('weights.mat')
    #x=data_0['weights']
    #original import gives a dictionary, then assign the value of the key 'weights' to x

    ##############################################################

def plot_coords(x):
	"""
		inputs: TxD matrix of observations
	           T-number of coords 
	           D-dimensionality of each observation
	           *Nans treated as missing observations
	    outputs: 1-, 2-, or 3-dimensional representation of the data
	"""
    
def main_helper(x):
	import numpy as np 
	import matplotlib.pyplot as plt 
	from mpl_toolkits.mplot3d import Axes3D
	from scipy.misc import lena
	#from plotting import *
	#from ggplot import *
	#import pandas as pd

	if x.shape[-1]==1:
	#shape gives (rows,columns) --> x.shape[-1] == # of columns 
	#in N-dimenional data, is the column # still displayed in the same position?
		bar(x)
		#^if #columns==1, make a bar graph (see 'bar')

	if x.shape[-1]==2:
		#if number of columns ==2
		scatter2D(x)
		#make a scatterplot

	if x.shape[-1]==3:
		# if 3 columns
		scatter3d(x)

	if x.shape[-1]>3:
		#columns>3

		#mean-center - find mean, excluding NaNs, then subtract mean from each value

		#remove all NaNs

		#do svd

		#let score=x*v

		#plot the first three columns of score



def bar(data):
    pos=np.arange(len(data))
    #arange returns evenly spaced values within given interval
    plt.xticks(pos+0.4, pos)
    #put ticks half way between each bar
    plt.bar(pos,data)
    #make the plot
    plt.show()
    #display it

def scatter2D(data):
	plt.scatter(data[:,0], data[:,1])
	#uses first two columns of the input data
	plt.show()

def scatter3d(data):
	fig = plt.figure()
	#ax=Axes3D(fig)
	ax = fig.add_subplot(111, projection='3d')
	ax.scatter(data[:,0], data[:,1], data[:,2], depthshade=True)
	#takes single input, dataset x, with 3 columns
	#uses each column as a dimension in 3d scatterplot
	plt.show()




<commit_msg>fixed commas in sample data<commit_after>	#FOR TESTING##################################################

	#OPTION 1 - 1D matrix
	#x=np.array([[1], [2], [3], [4]])
    
    #OPTION 2 - 2D matrix 
    #x=np.array([[1, 11], [2, 12], [3, 13], [4, 14]])

    #OPTION 3 - 3d matrix
    #x=np.array([[1, 11, 21], [2, 12, 22], [3, 13, 33], [4, 14, 44]])


    #OPTION 4 - use "weights" --> import weights.mat and assign to x as numpy.ndarray
	#import scipy.io 
	#data_0=scipy.io.loadmat('weights.mat')
    #x=data_0['weights']
    #original import gives a dictionary, then assign the value of the key 'weights' to x

    ##############################################################

def plot_coords(x):
	"""
		inputs: TxD matrix of observations
	           T-number of coords 
	           D-dimensionality of each observation
	           *Nans treated as missing observations
	    outputs: 1-, 2-, or 3-dimensional representation of the data
	"""
    
def main_helper(x):
	import numpy as np 
	import matplotlib.pyplot as plt 
	from mpl_toolkits.mplot3d import Axes3D
	from scipy.misc import lena
	#from plotting import *
	#from ggplot import *
	#import pandas as pd

	if x.shape[-1]==1:
	#shape gives (rows,columns) --> x.shape[-1] == # of columns 
	#in N-dimenional data, is the column # still displayed in the same position?
		bar(x)
		#^if #columns==1, make a bar graph (see 'bar')

	if x.shape[-1]==2:
		#if number of columns ==2
		scatter2D(x)
		#make a scatterplot

	if x.shape[-1]==3:
		# if 3 columns
		scatter3d(x)

	if x.shape[-1]>3:
		#columns>3

		#mean-center - find mean, excluding NaNs, then subtract mean from each value

		#remove all NaNs

		#do svd

		#let score=x*v

		#plot the first three columns of score

def bar(data):
	pos=np.arange(len(data))
	#arange returns evenly spaced values within given interval
	plt.xticks(pos+0.4, pos)
	#put ticks half way between each bar
	plt.bar(pos,data)
	#make the plot
	plt.show()
	#display it

def scatter2D(data):
	plt.scatter(data[:,0], data[:,1])
	#uses first two columns of the input data
	plt.show()

def scatter3d(data):
	fig = plt.figure()
	#ax=Axes3D(fig)
	ax = fig.add_subplot(111, projection='3d')
	ax.scatter(data[:,0], data[:,1], data[:,2], depthshade=True)
	#takes single input, dataset x, with 3 columns
	#uses each column as a dimension in 3d scatterplot
	plt.show()





{'fraction_non_alphanumeric': 0.17742279020234292, 'fraction_numerical': 0.03450479233226837, 'mean_word_length': 3.442762535477767, 'pattern_counts': {'":': 0, '<': 3, '<?xml version=': 0, '>': 11, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '7638991', 'n_tokens_mistral': 1835, 'n_tokens_neox': 1664, 'n_words': 544}