def bishleshan(category,topic): unigrams = nepunigrams() countername = defaultdict(int) print "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~"+topic.upper()+"~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~" for name,engnames in category.items(): bigramslist = [] bigrmsent = [] for word,count in unigrams: if name in word: countername[engnames] += count nepalibigrm = nepbigrams(name,sentlist) for bigrams,count in nepalibigrm[:4]: bigramslist.append(bigrams) for sentence in sentlist: if bigrams in sentence: bigrmsent.append(sentence.strip()) sentsscore = analyzescore(bigramslist,bigrmsent) print "\n++++++++++++++++++++++++++++++++++++++++++++++++SENTIMENT ANALYSIS OF WORD :"+name+"++++++++++++++++++++++++++++++++++++++++++++\n" print "In references to :",nepalibigrm[0][0]," OR ",nepalibigrm[1][0] print "==========================================================>>>>>>POSITIVE SENTENCES<<<<<<==========================================================\n" print "==========================================================>>>>>>>>>>>>>>><<<<<<<<<<<<<<<==========================================================\n" for line,score in sentsscore[:4]: print line,score,'\n' print "==========================================================>>>>>>NEGATIVE SENTENCES<<<<<<=========================================================\n" print "==========================================================>>>>>>>>>>>>>>><<<<<<<<<<<<<<<=========================================================\n" for line,score in sentsscore[-4:]: print line,score,'\n' topword = sorted(countername.items(),key=lambda x : x[1],reverse=True)[0][0] topname = category.keys()[category.values().index(topword)] namepie = piedata(topname) pieplot(namepie,"MONTHLY HITS ON WORD : "+category[topname]) plotdata(countername,"TOP NAMES TOPICS") return 0
def main(argv):
argid = {'func':['-id'], 'files':['simdata/svcsimn_d4a1_4.json'], '-m':['plus'], '-yl':['In-degree'], '-axisfsize':['large'], '-j':[1]}
argavgid = {'func':['-ad'], 'files':['simdata/svcsimn_d4a1_4.json'], '-m':['plus'], '-xl':['Timestep'], '-yl':['Average in-degree'], '-axisfsize':['large'], '-j':[150]}
#ax = p.plt.axes([0.13, 0.15, 0.6, 0.75])
p.plt.subplot(211)
p.plotdata(pl.loadarg([argid])
, xylabels={'x': pl.getargval(argid, '-xl', [''])[0], 'y': pl.getargval(argid, '-yl', [''])[0]}
, markset=pl.getargval(argid, '-m', ['var'])[0]
, isbase=False
, xlim=pl.getargval(argid, '-xlim')
, ylim=pl.getargval(argid, '-ylim')
, legloc=int(pl.getargval(argid, '-loc', ['1'])[0])
, ncol=int(pl.getargval(argid, '-ncol', ['1'])[0])
, numpoints=int(pl.getargval(argid, '-numpoints', ['1'])[0])
, axisfsize=pl.getaxisfsize(argid))
p.plt.subplot(212)
p.plotdata(pl.loadarg([argavgid])
, xylabels={'x': pl.getargval(argavgid, '-xl', [''])[0], 'y': pl.getargval(argavgid, '-yl', [''])[0]}
, markset=pl.getargval(argavgid, '-m', ['var'])[0]
, isbase=False
, xlim=pl.getargval(argavgid, '-xlim')
, ylim=pl.getargval(argavgid, '-ylim')
, legloc=int(pl.getargval(argavgid, '-loc', ['1'])[0])
, ncol=int(pl.getargval(argavgid, '-ncol', ['1'])[0])
, numpoints=int(pl.getargval(argavgid, '-numpoints', ['1'])[0])
, axisfsize=pl.getaxisfsize(argavgid))
p.plt.gcf().suptitle('In-degree of each random failed service (top) and the average in-degree\n of the available services (bottom) during cascading failure simulation', fontsize=14)
p.processplot(p.plt)
theta = np.zeros([2,1])
iterations = 1500
alpha = 0.01
ones = np.ones((m,1))
X = np.hstack((ones, X)) # adding the intercept term
J = costfunction.cost(X, y, theta)
print(J)
theta, J_history = gradientdes.gradientDescent(X, y, theta, alpha, iterations)
print(theta)
J = costfunction.cost(X, y, theta)
print(J)
plot.plotdata(X,y,theta)
predict1 = np.dot([1, 3.5], theta)
print('For population = 35,000, we predict a profit of')
print(predict1*10000)
predict2 = np.dot([1, 7], theta)
print('For population = 70,000, we predict a profit of')
print(predict2*10000)
j_plot.j_plot(X, y, theta)
def main(argv):
lsarg = []
lsarg.append({'func':['-pl'], 'files':['simdata/plotfunceff_tri-sf_d2_d8.json'], '-m':['reddia-'], '-l':['scale-free']})
lsarg.append({'func':['-pl'], 'files':['simdata/plotfunceff_tri-exp_d2_d8.json'], '-m':['bluepenta-'], '-l':['exponential']})
lsarg.append({'func':['-pl'], 'files':['simdata/plotfunceff_tri-rand_d2_d8.json'], '-m':['trihexaorg-'], '-l':['random'], '-t':['The effect of $\langle dep \\rangle$ on cascading failure in service networks'], '-xl':['Degree of dependency $\langle dep \\rangle$'], '-yl':['Cascade failed services $n_c$ (in fraction)'], '-axisfsize':['large'], '-ncol':[3], '-loc':[2]})
for i in range(len(lsarg)):
ds = []
func = lsarg[i]['func'][0]
step = p.STEP
argstep = getargval(lsarg[i], '-j')
if argstep:
step = int(argstep[0])
with open(lsarg[i]['files'][0]) as f:
ds = json.load(f)
logx = False
arglogx = getargval(lsarg[i], '-logx')
if arglogx:
logx = int(arglogx[0]) > 0
logy = False
arglogy = getargval(lsarg[i], '-logy')
if arglogy:
logy = int(arglogy[0]) > 0
lb = p.LOGBINBASE
arglb = getargval(lsarg[i], '-lb')
if arglb:
lb = float(arglb[0])
xlim = getargval(lsarg[i], '-xlim')
ylim = getargval(lsarg[i], '-ylim')
plotfile = getargval(lsarg[i], '-v', [None])[0]
axisfsize = getargval(lsarg[i], '-axisfsize', ['medium'])[0]
try:
axisfsize = int(axisfsize)
except:
pass
ncol = int(getargval(lsarg[i], '-ncol', [1])[0])
numpoints = int(getargval(lsarg[i], '-numpoints', [1])[0])
p.plotdata(ds
, getargval(lsarg[i], '-l')
, getargval(lsarg[i], '-t', [''])[0]
, {'x': getargval(lsarg[i], '-xl', [''])[0], 'y': getargval(lsarg[i], '-yl', [''])[0]}
, getargval(lsarg[i], '-m', ['var'])[0]
, isbase=False
, logx=logx
, logy=logy
, xlim=xlim
, ylim=ylim
, legloc=int(getargval(lsarg[i], '-loc', ['1'])[0])
, ncol=ncol
, numpoints=numpoints
, axisfsize=axisfsize
, ax=p.plt.gca())
p.processplot(p.plt, getargval(lsarg[i], '-s', [None])[0])
import pandas as pd
import scipy.optimize as op
import matplotlib.pyplot as plt
import plot
import costfunction
import gradient
import accuracy
data = pd.read_csv('Data/ex2data1.txt', header=None)
X = data.iloc[:, [0, 1]]
y = data.iloc[:, 2]
m = len(y)
print(data.head())
plot.plotdata(X, y)
(m, n) = X.shape
y = y[:, np.newaxis]
ones = np.ones((m, 1))
X = np.hstack((ones, X))
#theta = np.zeros((X.shape[1],1))
#c = costfunction.cost(X, y, theta)
#print(c)
#test_theta = [[-24], [0.2], [0.2]]
#print(np.shape(test_theta))
#print(gradient.grad(X, y, test_theta))
#X = np.array([[1,2,3], [1,3,4]])