def showErrorBars(population, sizes, numTrials):
xVals = []
sizeMeans, sizeSDs = [], []
for sampleSize in sizes:
xVals.append(sampleSize)
trialMeans = []
for t in range(numTrials):
sample = random.sample(population, sampleSize)
popMean, sampleMean, popSD, sampleSD =\
getMeansAndSDs(population, sample)
trialMeans.append(sampleMean)
sizeMeans.append(sum(trialMeans) / len(trialMeans))
sizeSDs.append(numpy.std(trialMeans))
print(sizeSDs)
numpy.errorbar(xVals,
sizeMeans,
yerr=1.96 * numpy.array(sizeSDs),
fmt='o',
label='95% Confidence Interval')
numpy.title('Mean Temperature (' + str(numTrials) + ' trials)')
numpy.xlabel('Sample Size')
numpy.ylabel('Mean')
numpy.axhline(y=popMean, color='r', label='Population Mean')
numpy.xlim(0, sizes[-1] + 10)
numpy.legend()
def plotFrequency(series, sampRate):
""" Plot a frequency to a graph """
s = series / (2.**15)
p, freqArray = _fastFourier(s, sampRate)
np.plot(freqArray / 1000, 10 * pl.log10(p), color='k')
np.xlabel('Frequency (kHz)')
np.ylabel('Power (dB)')
np.plt.show()
return
def simAll(drunkKinds, walkLengths, numTrials):
styleChoice = styleIterator(('m-', 'b--', 'g-.'))
for dClass in drunkKinds:
curStyle = styleChoice.nextStyle()
print('Starting simulation of', dClass.__name__)
means = simDrunk(numTrials, dClass, walkLengths)
numpy.plot(walkLengths, means, curStyle, label=dClass.__name__)
numpy.title('Mean Distance from Origin (' + str(numTrials) + ' trials)')
numpy.xlabel('Number of Steps')
numpy.ylabel('Distance from Origin')
numpy.legend(loc='best')
def plotTone(series, sampRate):
""" Plot a tone to a graph """
# Convert sound array to floating point between -1 to 1
snd = series / (2.**15)
timeArray = pl.arange(0, snd.shape[0])
timeArray = timeArray / sampRate
timeArray = timeArray * 1000 # scales to milliseconds
# Plot the tone graph
np.plot(timeArray, snd, color='k')
np.ylabel('Amplitude')
np.xlabel('Time (ms)')
np.plt.show()
return
def traceWalk(fieldKinds, numSteps):
styleChoice = styleIterator(('b+', 'r^', 'ko'))
for fClass in fieldKinds:
d = UsualDrunk()
f = fClass()
f.addDrunk(d, Location(0, 0))
locs = []
for s in range(numSteps):
f.moveDrunk(d)
locs.append(f.getLoc(d))
xVals, yVals = [], []
for loc in locs:
xVals.append(loc.getX())
yVals.append(loc.getY())
curStyle = styleChoice.nextStyle()
numpy.plot(xVals, yVals, curStyle, label=fClass.__name__)
numpy.title('Spots Visited on Walk (' + str(numSteps) + ' steps)')
numpy.xlabel('Steps East/West of Origin')
numpy.ylabel('Steps North/South of Origin')
numpy.legend(loc='best')
def plotLocs(drunkKinds, numSteps, numTrials):
styleChoice = styleIterator(('k+', 'r^', 'mo'))
for dClass in drunkKinds:
locs = getFinalLocs(numSteps, numTrials, dClass)
xVals, yVals = [], []
for loc in locs:
xVals.append(loc.getX())
yVals.append(loc.getY())
xVals = numpy.array(xVals)
yVals = numpy.array(yVals)
meanX = sum(abs(xVals)) / len(xVals)
meanY = sum(abs(yVals)) / len(yVals)
curStyle = styleChoice.nextStyle()
numpy.plot(xVals, yVals, curStyle,
label = dClass.__name__ +\
' mean abs dist = <'
+ str(meanX) + ', ' + str(meanY) + '>')
numpy.title('Location at End of Walks (' + str(numSteps) + ' steps)')
numpy.ylim(-1000, 1000)
numpy.xlim(-1000, 1000)
numpy.xlabel('Steps East/West of Origin')
numpy.ylabel('Steps North/South of Origin')
numpy.legend(loc='lower center')
# #plt.title('Accuracy for different Momentum for CNN')
# plt.ylabel('Error')
# #plt.ylabel('Accuracy')
# plt.xlabel('Momentum')
# plt.show()
# #For Batch Size
# plt.xticks([1 ,2, 3, 4, 5],['1','10','100','500','1000'])
# plt.plot([1 ,2, 3, 4, 5],[1.88155 ,1.25564 ,0.43665 ,1.11511 ,3.02981 ], 'bo', label='Training')
# plt.plot([1 ,2, 3, 4, 5],[1.88067 ,1.71583 ,0.63323 ,1.07845 ,2.84858 ], 'go', label='Validation')
# plt.axis([0, 5.5, 0.0, 3.2])
# plt.legend(loc=0)
# plt.title('Cross-Entropy for different batch sizes for CNN')
# #plt.title('Accuracy for different batch sizes for CNN')
# plt.ylabel('Error')
# #plt.ylabel('Accuracy')
# plt.xlabel('Batch Size')
# plt.show()
#For 3.3
plt.xticks([1, 2, 3], ['[2 16]', '[15 16]', '[30 16]'])
plt.plot([1, 2, 3], [0.28542, 0.74748, 0.28542], 'bo', label='Training')
plt.plot([1, 2, 3], [0.27924, 0.70883, 0.27924], 'go', label='Validation')
plt.axis([0.5, 3.5, 0, 0.8])
plt.legend(loc=0)
#plt.title('Cross-Entropy for different number of filters in the first layer of CNN')
plt.title('Accuracy for different number of filters in the first layer of CNN')
#plt.ylabel('Error')
plt.ylabel('Accuracy')
plt.xlabel('Number of Units')
plt.show()
numpy.hist(means,
numBins,
color=color,
label=legend,
weights=[1 / len(means)] * len(means),
hatch=style)
return getMeanAndStd(means)
mean, std = plotMeans(1, 1000000, 19, '1 die', 'b', '')
print('Mean of rolling 1 die =', str(mean) + ',', 'Std =', std)
mean, std = plotMeans(50, 1000000, 19, 'Mean of 50 dice', 'r', '')
print('Mean of rolling 50 dice =', str(mean) + ',', 'Std =', std)
numpy.title('Rolling Continuous Dice')
numpy.xlabel('Value')
numpy.ylabel('Probability')
numpy.legend()
##Test CLT
# numTrials = 100000
# numSpins = 2000
# game = FairRoulette()
# means = []
# for i in range(numTrials):
# means.append(findPocketReturn(game, 1, numSpins,
# False)[0])
# numpy.hist(means, bins = 19,
# weights = [1/len(means)]*len(means))
# numpy.xlabel('Mean Return')
def makeHist(data, title, xlabel, ylabel, bins=20):
numpy.hist(data, bins=bins)
numpy.title(title)
numpy.xlabel(xlabel)
numpy.ylabel(ylabel)
def plotDiffs(sampleSizes, diffs, title, label, color='b'):
numpy.plot(sampleSizes, diffs, label=label, color=color)
numpy.xlabel('Sample Size')
numpy.ylabel('% Difference in SD')
numpy.title(title)
numpy.legend()
for count in np.mslice[1:C]:
code_sent = Codewords(count, np.mslice[:]).T
F = symbols_est(np.mslice[(sub_block - 1) * n + 1:sub_block * n], fr)
H = fading_coeffs(np.mslice[(sub_block - 1) * n + 1:sub_block * n], fr)
# Detect_output(count,1) = sum(abs(F - code_sent.*H).^2); %if no equalization (only for OFDM)
Detect_output(count, 1).lvalue = sum(abs(F - code_sent) ** np.elpow ** 2)
# end
# Determine Data_sent from minimum
Minimum = min(Detect_output)
# end
# end
# % Demapping
Data_out_reshape = np.reshape(Data_out, np.mcat([]), 1)
symbols_estimated = np.reshape(Codewords(Data_out_reshape, np.mslice[:]).T, np.mcat([]), Frames)
OutputBinaryp = CodewordsIntoBits(symbols_estimated, M, n, k, g, Frames)
OutputBinary = np.reshape(OutputBinaryp, 1, np.mcat([]))
# % BER Calculation
Nbre_error = np.length(np.find(np.reshape(InputBinaryS, 1, np.mcat([])) - OutputBinary != 0)) # Number of errors
ber(j).lvalue = Nbre_error / (Frames * m) # number of errors per number of total bit
# end SNR
# %% BER Plot
np.semilogy(EbNo_dB, ber, np.mstring('d-b'), np.mstring('linewidth'), 2)
np.hold(np.mstring('on'))
np.grid(np.mstring('on'))
np.hold(np.mstring('on'))
np.xlim(np.mcat([0, 25]))
np.ylim(np.mcat([1e-6, 1]))
np.xlabel(np.mstring('Eb/N0 in dB'))
np.ylabel(np.mstring('BER'))
# In[44]: Soal1
import pandas as choi #melakukan import pada library pandas sebagai arjun
laptop = {
"Nama Laptop": ['Asus', 'ROG', 'Lenovo', 'Samsung']
} #membuat varibel yang bernama laptop, dan mengisi dataframe nama2 laptop
x = Arjun.DataFrame(
laptop
) #variabel x membuat DataFrame dari library pandas dan akan memanggil variabel laptop.
print(' Arjun Punya Laptop ' + x) #print hasil dari x
# In[44]: Soal2
import numpy as Arjun #melakukan import numpy sebagai arjun
matrix_x = Arjun.eye(
10) #membuat matrix dengan numpy dengan menggunakan fungsi eye
matrix_x #deklrasikan matrix_x yang telah dibuat
print(matrix_x) #print matrix_x yang telah dibuat dengan 10x10
# In[44]: Soal3
import matplotlib.pyplot as Arjun #import matploblib sebagai arjun
Arjun.plot([1, 1, 7, 4, 0, 2,
1]) #memberikan nilai plot atau grafik pada arjun
Arjun.xlabel('Arjun Yuda Firwanda') #memberikan label pada x
Arjun.ylabel('1174008') #memberikan label pada y
Arjun.show() #print hasil plot berbentuk grafik