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generate_text.py
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import random_idx
import utils
import numpy as np
import string
import pandas as pd
import matplotlib.pyplot as plt
N = 10000
k = 5000
cluster_sz = [3]
ordered = 1
#alph = 'abc'
alph = string.lowercase + ' '
def gen_lets(N=N,k=k):
# generate letter vectors
RI_letters = random_idx.generate_letter_id_vectors(N,k)
RI_letters_n = RI_letters/np.linalg.norm(RI_letters)
return RI_letters, RI_letters_n
def create_english_vec(N=N,k=k, cluster_sz = [2]):
print "generating english vector of cluster size", cluster_sz
total_eng = np.zeros((1,N))
# generate english vector
for cz in cluster_sz:
english_vector = random_idx.generate_RI_lang(N, RI_letters, cz, ordered, languages=['eng'])
total_eng += english_vector
normed_eng = total_eng/np.linalg.norm(total_eng)
return total_eng, normed_eng
RI_letters, RI_letters_n = gen_lets()
english_vector, normed_eng = create_english_vec(cluster_sz=cluster_sz)
def generate_words(cluster_sz, english_vector=english_vector, normed_eng=normed_eng):
print "generating english vector of cluster size", cluster_sz
# generate english vector
#english_vector = random_idx.generate_RI_text_words(N, RI_letters, './lang_texts/texts_english/eng.txt')
# generate new string of letters
length = 30
alphy = utils.generate_ordered_clusters(alph, cluster_sz=cluster_sz)
gstr = alph[np.random.randint(len(alph))]
temp_str = gstr
for i in xrange(length):
max_idx = 0
maxabs = 0
for j in xrange(len(alphy)):
temp_str = gstr + alphy[j]
temp_id = random_idx.generate_RI_str(N,RI_letters,cluster_sz,ordered,temp_str)
#temp_id += 1e1*np.random.randn(1,N)
temp_id /= np.linalg.norm(temp_id)
absy = np.abs(temp_id.dot(normed_eng.T))
#print temp_str, absy
if absy > maxabs:
max_idx = j
maxabs = absy
gstr += alphy[max_idx]
print len(gstr), maxabs, gstr
def generate_RI_block_vectors(cz=2):
# generate letter block values for total comparison
RI_blocks = utils.generate_ordered_clusters(alph,cluster_sz=cz)
#print RI_blocks
RI_block_vectors = np.zeros((len(RI_blocks),N))
for i in xrange(len(RI_blocks)):
block = RI_blocks[i]
#print block
block_vec = random_idx.id_vector(N,block,alph, RI_letters,ordered=ordered)
RI_block_vectors[i,:] = block_vec
return RI_blocks, RI_block_vectors
def find_letter_partner(test_letter, english_vector=english_vector):
# testing letter blocks for their "block partners"
test_vec = random_idx.id_vector(N,test_letter,alph, RI_letters, ordered=ordered)
#test_vec = test_vec/np.linalg.norm(test_vec)
'''
sub_eng = np.copy(english_vector)
for r in xrange(len(blocks)):
block = blocks[r]
if test_letter != block[0]:
sub_eng[:, RI_blocks[r,:] != 0] = 1e-2
print sub_eng
'''
cz = len(test_letter)
#if cz > 1:
# for i in xrange(len(alph)):
# english_vector -= RI_letters[i,:]
#english_vector /= np.linalg.norm(english_vector)
#factored_eng = np.multiply(english_vector, np.roll(letter, 1))
factored_eng = np.multiply(english_vector, np.roll(test_vec, 1))
#factored_eng = np.roll(np.multiply(english_vector, letter), -1)
#factored_RI_letters = RI_letters, np.roll(letter,1))
#if len(test_letter) == 1:
likely_block_partner, values, partners = utils.find_language(test_letter, factored_eng, RI_letters, alph, display=1)
'''else:
#RI_blocks, RI_block_vectors = generate_RI_block_vectors(cz=cz)
#likely_block_partner, values, partners = utils.find_language(test_letter, factored_eng, RI_block_vectors, RI_blocks, display=1)
'''
return likely_block_partner, values, partners
block_list = ['th']
for block in block_list:
likely_block_partner, values, partners = find_letter_partner(block)
'''
th_partners = []
for l in alph:
th_partners.append(letter+l)
print th_partners
print len(th_partners)
comparison = np.empty((len(alph),2))
base_comparison = [8.49, 5.65, 7.46, 3.53, 9.01, 4.64, 3.66, 8.63, 8.96, 3.09, 3.61, 6.35, 5.49, 5.56, 8.55, 3.85, 0, 7.94, 8.79, 7.30, 6.67, 7.32, 5.02, 3.83, 7.60, 4.83]
for i in xrange(len(base_comparison)):
comparison[i,0] = base_comparison[i]
zippy = zip(values,partners)
zippy.sort(key=lambda x: x[1])
print zippy
for i in xrange(len(zippy)):
comparison[i,1] = zippy[i][0]
print comparison
# normalize
for i in xrange(comparison.shape[1]):
comparison[:,i] = (comparison[:,i] - np.min(comparison[:,i]))/(np.max(comparison[:,i]) - np.min(comparison[:,i]))
df2 = pd.DataFrame(comparison, columns=['objective freq', 'high-D encoding'])
ax = df2.plot(kind='bar')
ax.set_xticklabels(th_partners)
plt.show()
'''
'''
print "ri letters"
for x in xrange(len(alph)):
print alph[x]
print RI_letters[x,:]
print "eng vec"
print english_vector
print 'factored eng'
print factored_eng
print 'diff'
print english_vector - factored_eng
'''
#similar_letter = utils.find_language('', english_vector, RI_letters, alph, display=1)
'''
# vague attempt at generating text over different parameters
for cz in xrange(2,3):#3+1):
generate_words(cz)
'''