def period_guess(self, y_s, t_0):
extrema = []
spacings = [log(extrema[i+1] - t_0) - log(extrema[i] - t_0)
for i in range(1, len(extrema))]
omegas = spacings / 2 * 22 / 7
omega_guess = average(omegas)
omega_std = std(omegas)
return omega_guess, omega_std
def main():
os.chdir(r'G:\Shared drives\Apex\Acoustic Data\IOA, conference proceedings\2021 papers\Max noise levels from hockey pitches')
# adjust_value_by_LAeqT()
LAFmax = []
filenames = [str(1+n)+'.wav' for n in range(104)]
LAFmax = [calc_LAeq_dt(os.path.join('bat hitting', fn)) for fn in filenames]
hist_plot(LAFmax)
print(mean(LAFmax))
print(std(LAFmax))
plt.xlabel('LAFmax at 11 m')
plt.show()
def analyze_sites(some_list): ''' requires the following input format: seq score cdist cons cdist = distance to center, cons = conservation value ''' # column definitions for reference / calling x0 = 0 # x0 is sequence x1 = 1 # x1 is score (Ri) x2 = 2 # x2 is peaklength x3 = 3 # x3 is cdist x4 = 4 # x4 is conservation score # columns are seq, score, peaklength, cdist, cons # build an array scalar with the correct column types for x in some_list: # forces sequences to uppercase x[x0] = x[x0].upper() # forces length to int x[x2] = int(x[x2]) # forces cdist to int x[x3] = int(x[x3]) # forces cons score to floating point # and correct nan if x[x4]=='nan': x[x4] = 0.0 else: x[x4] = float(x[x4]) # sort the list by sequence (arbitrarily) sorted_list = sorted(some_list, key=operator.itemgetter(x0)) # Part 3: continue with analysis analyzedSeqs = [] counter = len(sorted_list) while counter > 0: site = sorted_list.pop() counter -= 1 seq = site[x0] score = site[x1] # start a list of dists, absdists, cscores, we'll sum later i = 1 plengths = [ site[x2] ] dists = [ site[x3] ] absdists = [ abs(site[x3]) ] cscores = [ site[x4] ] while True: # grab sequences that match try: y = sorted_list.pop() except IndexError: break counter -= 1 # look if we match the last entry if y[x0] == seq: i += 1 plengths.append(y[x2]) dists.append(y[x3]) absdists.append( abs( y[x3] ) ) cscores.append(y[x4]) # if we don't, escape to previous loop else: # append back sorted_list.append(y) break # append seq, sum of scores, # of instances avgPlength = float(sum(plengths))/i avgDist = float(sum(dists))/i avgAbsdist = float(sum(absdists))/i distErr = std(absdists) analyzedSeqs.append([seq, score, i, sum(cscores), avgPlength, avgDist, avgAbsdist,distErr]) analyzedSeqs.reverse() return sorted(analyzedSeqs, key=operator.itemgetter(x2))
def biaser(standings):
return scale * std(standings, ddof=1)
def biaser(standings):
return scale * std(standings, ddof=1)
def svr(C, gamma, eps):
#initialization of data wmproxy
traininput, traintarget, testinput, testtarget = initialize_wmproxy()
#training of the SVR
#scaling values in training and test targets
for i in range(len(traintarget)):
if(traintarget[i] != 0):
traintarget[i] = log(traintarget[i])
if(traininput[i] != 0):
traininput[i] = log(traininput[i])
for i in range(len(testtarget)):
if(testtarget[i] != 0):
testtarget[i] = log(testtarget[i])
if(testinput[i] != 0):
testinput[i] = log(testinput[i])
avg = mean(traintarget)
sigma = std(traintarget)
maxtrain = len(traintarget)
C = max([abs(avg + sigma), abs(avg - sigma)])
print "C is equal to %f" % C
svr = SVR(traininput[maxtrain-1440:maxtrain], testinput, traintarget[maxtrain-1440:maxtrain],gamma,C,eps,eps)
out = svr.svr_req(testinput[0:30])
error = 0
for i in range(len(out)):
error += (out[i] - testtarget[i])
mean_error = error / len(out)
variance = 0
for i in range(len(out)):
variance = abs(out[i] - mean_error)
variance /= len(out)
print "Variance = %f" % variance
epsilon = 3*variance*sqrt(log(len(out))/len(out))
print "Epsilon = %f" % epsilon
#calculation of the metrics
sme = svr.calc_sme(testtarget[0:30], out)
mape = svr.calc_mape(out, testtarget[0:30])
predx = svr.calc_pred(out, testtarget[0:30], 25)
rsq = svr.calc_rsqr(out, testtarget[0:30])
print out
print testtarget[0:30]
# print model results!
x = array(testinput[0:30], dtype=int32)
y = array(testtarget[0:30], dtype=int32)
xp = array(testinput[0:30], dtype=int32)
yp = array(out, dtype=int32)
fig = figure()
ax1 = fig.add_subplot(1,1,1)
ax1.title.set_text("Predizioni modello SVR con C= %f, Gamma = %f, Eps = %f" % (C, gamma, eps))
realvalues = ax1.plot(x, y)
predictedvalues = ax1.plot(xp,yp,"r")
ax1.axis([8.9,max(xp)+0.5,0,max(y)+10])
ax1.set_xlabel('minutes of the week')
ax1.set_ylabel('number of requests')
legend([realvalues,predictedvalues], ["Real Values","Predicted Values"])
fig.savefig("svr_model_%f" % time(), format='png')
print "SME = %f" % sme
print "MAPE = %f" % mape
print "R^2 = %f" % rsq
print "PREDX = %f" % predx
rewardedTrials = np.zeros((len(prob), len(vol), nEpisodes))
totalIter = len(prob) * len(vol) * nEpisodes
n = 1 #iterations counter
now = datetime.now()
print 'Simulation started. Date:', now.strftime("%Y-%m-%d %H:%M:%S")
for v in xrange(len(vol)):
for p in xrange(len(prob)):
environment = loadArrangeVar(prob[p], vol[v], path, 'environment')
for e in xrange(nEpisodes):
lrAgent = loadEpisodeVar(prob[p], vol[v], e, path, 'lrAgent')
agent = loadEpisodeVar(prob[p], vol[v], e, path, 'agent')
chosenLRs = [lrAgent.lbd[a] for a in lrAgent.actions]
lbdMean[p][v][e] = mean(chosenLRs)
lbdStd[p][v][e] = std(
chosenLRs
) #low std indicates the algorithm chooses a optimum learning rate - convergence
meanSquareError[p][v][e] = mean(lrAgent.r)
rightEstimate[p][v][e] = np.sum(
around(agent.x[1:]) == around(environment.history)
) / environment.history.size * 100 #x has a shape of nTrials+1 and history of nTrials. That is because after the last trial the agent learns the value of x for the next
rightPrediction[p][v][e] = np.sum(
around(agent.x[0:-1]) == around(
environment.history)) / environment.history.size * 100
rewardedTrials[p][v][e] = float(
np.sum(agent.r)
) / agent.x.size * 100 #Calculates how often the agent was rewarded within the episode
showProgress(totalIter, n, time.time(), begin)
n += 1