def mc_test():
import os
import numpy as np
from pyemu import MonteCarlo, Cov
jco = os.path.join("pst","pest.jcb")
pst = jco.replace(".jcb",".pst")
out_dir = os.path.join("mc")
if not os.path.exists(out_dir):
os.mkdir(out_dir)
#write testing
mc = MonteCarlo(jco=jco,verbose=True,sigma_range=6)
cov = Cov.from_parameter_data(mc.pst,sigma_range=6)
assert np.abs((mc.parcov.x - cov.x).sum()) == 0.0
mc.draw(10,obs=True)
mc.write_psts(os.path.join("temp","real_"))
mc.parensemble.to_parfiles(os.path.join("mc","real_"))
mc = MonteCarlo(jco=jco,verbose=True)
mc.draw(10,obs=True)
print("prior ensemble variance:",
np.var(mc.parensemble.loc[:,"mult1"]))
projected_en = mc.project_parensemble(inplace=False)
print("projected ensemble variance:",
np.var(projected_en.loc[:,"mult1"]))
import pyemu
sc = pyemu.Schur(jco=jco)
mc = MonteCarlo(pst=pst,parcov=sc.posterior_parameter,verbose=True)
mc.draw(10)
print("posterior ensemble variance:",
np.var(mc.parensemble.loc[:,"mult1"]))
def test_mat_output(self):
samples = GMM1([.9999, .0001], [0.0, 1.0], [0.000001, 0.000001],
rng=self.rng,
size=[40, 20])
assert samples.shape == (40, 20)
assert -.001 < np.mean(samples) < .001, np.mean(samples)
assert np.var(samples) < .0001, np.var(samples)
def testPdfOfSampleMultiDims(self):
student = student_t.StudentT(df=[7., 11.], loc=[[5.], [6.]], scale=3.)
self.assertAllEqual([], student.event_shape)
self.assertAllEqual([], self.evaluate(student.event_shape_tensor()))
self.assertAllEqual([2, 2], student.batch_shape)
self.assertAllEqual([2, 2], self.evaluate(student.batch_shape_tensor()))
num = 50000
samples = student.sample(num, seed=123456)
pdfs = student.prob(samples)
sample_vals, pdf_vals = self.evaluate([samples, pdfs])
self.assertEqual(samples.get_shape(), (num, 2, 2))
self.assertEqual(pdfs.get_shape(), (num, 2, 2))
self.assertNear(5., np.mean(sample_vals[:, 0, :]), err=.03)
self.assertNear(6., np.mean(sample_vals[:, 1, :]), err=.03)
self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:, 0, 0], err=0.02)
self._assertIntegral(sample_vals[:, 0, 1], pdf_vals[:, 0, 1], err=0.02)
self._assertIntegral(sample_vals[:, 1, 0], pdf_vals[:, 1, 0], err=0.02)
self._assertIntegral(sample_vals[:, 1, 1], pdf_vals[:, 1, 1], err=0.02)
if not stats:
return
self.assertNear(
stats.t.var(7., loc=0., scale=3.), # loc d.n. effect var
np.var(sample_vals[:, :, 0]),
err=.4)
self.assertNear(
stats.t.var(11., loc=0., scale=3.), # loc d.n. effect var
np.var(sample_vals[:, :, 1]),
err=.4)
def _call(self, dataset):
"""Computes featurewise scores."""
attrdata = dataset.sa[self.__attr].value
if np.issubdtype(attrdata.dtype, 'c'):
raise ValueError("Correlation coefficent measure is not meaningful "
"for datasets with literal labels.")
samples = dataset.samples
pvalue_index = self.__pvalue
result = np.empty((dataset.nfeatures,), dtype=float)
for ifeature in xrange(dataset.nfeatures):
samples_ = samples[:, ifeature]
corr = pearsonr(samples_, attrdata)
corrv = corr[pvalue_index]
# Should be safe to assume 0 corr_coef (or 1 pvalue) if value
# is actually NaN, although it might not be the case (covar of
# 2 constants would be NaN although should be 1)
if np.isnan(corrv):
if np.var(samples_) == 0.0 and np.var(attrdata) == 0.0 \
and len(samples_):
# constant terms
corrv = 1.0 - pvalue_index
else:
corrv = pvalue_index
result[ifeature] = corrv
return Dataset(result[np.newaxis])
def average_data(data):
"""
Find mean and std. deviation of data returned by ``simulate``.
"""
numnodes = data['nodes']
its = data['its']
its_mean = numpy.average(its)
its_std = math.sqrt(numpy.var(its))
dead = data['dead']
dead_mean = 100.0*numpy.average(dead)/numnodes
dead_std = 100.0*math.sqrt(numpy.var(dead))/numnodes
immune = data['immune']
immune_mean = 100.0*numpy.average(immune)/numnodes
immune_std = 100.0*math.sqrt(numpy.var(immune))/numnodes
max_contam = data['max_contam']
max_contam_mean = 100.0*numpy.average(max_contam)/numnodes
max_contam_std = 100.0*math.sqrt(numpy.var(max_contam))/numnodes
normal = data['normal']
normal_mean = 100.0*numpy.average(normal)/numnodes
normal_std = 100.0*math.sqrt(numpy.var(normal))/numnodes
return {'its': (its_mean, its_std),
'nodes': numnodes,
'dead': (dead_mean, dead_std),
'immune': (immune_mean, immune_std),
'max_contam': (max_contam_mean, max_contam_std),
'normal': (normal_mean, normal_std)}
def featArray(data):
sh = n.shape(data)
freqs = n.linspace(0,sh[1],sh[1])
NNvar = n.zeros_like(data)
dvar = n.var(n.abs(data))
for i in range(sh[0]):
for j in range(sh[1]):
#samples = []
#for p in range(100):
k = n.random.randint(-1,1,size=1000)
l = n.random.randint(-1,1,size=1000)
# try:
#samples = n.abs(data[k+i,l+j])
# except:
# pass
NNvar[i,j] = n.var(n.abs(data[k+i,l+j]))
X1 = n.zeros((sh[0]*sh[1],3))
X1[:,0] = (n.real(data)).reshape(sh[0]*sh[1])
X1[:,1] = (n.imag(data)).reshape(sh[0]*sh[1])
#X1[:,2] = (n.log10(n.abs(NNvar)) - n.median(n.log10(n.abs(NNvar)))).reshape(sh[0]*sh[1])
NNvar = NNvar - n.median(NNvar)
X1[:,2] = (n.log10(n.abs(NNvar))).reshape(sh[0]*sh[1])
#X1[:,3] = (n.array([freqs]*sh[0])).reshape(sh[0]*sh[1])
#X1[:,4] = (n.array([times]*sh[1])).reshape(sh[0]*sh[1])
X1[n.abs(X1)>10**100] = 0
for m in range(X1.shape[1]):
X1[:,m] = X1[:,m]/n.abs(X1[:,m]).max()
X1 = n.nan_to_num(X1)
return X1
def r2_score(y_true, y_pred, round_to=2):
R"""R-squared for Bayesian regression models. Only valid for linear models.
http://www.stat.columbia.edu/%7Egelman/research/unpublished/bayes_R2.pdf
Parameters
----------
y_true: : array-like of shape = (n_samples) or (n_samples, n_outputs)
Ground truth (correct) target values.
y_pred : array-like of shape = (n_samples) or (n_samples, n_outputs)
Estimated target values.
round_to : int
Number of decimals used to round results (default 2).
Returns
-------
`namedtuple` with the following elements:
R2_median: median of the Bayesian R2
R2_mean: mean of the Bayesian R2
R2_std: standard deviation of the Bayesian R2
"""
dimension = None
if y_true.ndim > 1:
dimension = 1
var_y_est = np.var(y_pred, axis=dimension)
var_e = np.var(y_true - y_pred, axis=dimension)
r2 = var_y_est / (var_y_est + var_e)
r2_median = np.around(np.median(r2), round_to)
r2_mean = np.around(np.mean(r2), round_to)
r2_std = np.around(np.std(r2), round_to)
r2_r = namedtuple('r2_r', 'r2_median, r2_mean, r2_std')
return r2_r(r2_median, r2_mean, r2_std)
def test_hash_functions():
# Checks randomness of hash functions.
# Variance and mean of each hash function (projection vector)
# should be different from flattened array of hash functions.
# If hash functions are not randomly built (seeded with
# same value), variances and means of all functions are equal.
n_samples = 12
n_features = 2
n_estimators = 5
rng = np.random.RandomState(42)
X = rng.rand(n_samples, n_features)
lshf = ignore_warnings(LSHForest, category=DeprecationWarning)(
n_estimators=n_estimators,
random_state=rng.randint(0, np.iinfo(np.int32).max))
ignore_warnings(lshf.fit)(X)
hash_functions = []
for i in range(n_estimators):
hash_functions.append(lshf.hash_functions_[i].components_)
for i in range(n_estimators):
assert_not_equal(np.var(hash_functions),
np.var(lshf.hash_functions_[i].components_))
for i in range(n_estimators):
assert_not_equal(np.mean(hash_functions),
np.mean(lshf.hash_functions_[i].components_))
def test_bernoulli_extract(self):
fit = self.fit
extr = fit.extract(permuted=True)
assert -7.4 < np.mean(extr['lp__']) < -7.0
assert 0.1 < np.mean(extr['theta']) < 0.4
assert 0.01 < np.var(extr['theta']) < 0.02
# use __getitem__
assert -7.4 < np.mean(fit['lp__']) < -7.0
assert 0.1 < np.mean(fit['theta']) < 0.4
assert 0.01 < np.var(fit['theta']) < 0.02
# permuted=False
extr = fit.extract(permuted=False)
self.assertEqual(extr.shape, (1000, 4, 2))
self.assertTrue(0.1 < np.mean(extr[:, 0, 0]) < 0.4)
# permuted=True
extr = fit.extract('lp__', permuted=True)
assert -7.4 < np.mean(extr['lp__']) < -7.0
extr = fit.extract('theta', permuted=True)
assert 0.1 < np.mean(extr['theta']) < 0.4
assert 0.01 < np.var(extr['theta']) < 0.02
extr = fit.extract('theta', permuted=False)
assert extr.shape == (1000, 4, 2)
assert 0.1 < np.mean(extr[:, 0, 0]) < 0.4
def test_reductions():
assert compute(t.a.sum(), b) == 6
assert compute(t.a.min(), b) == 1
assert compute(t.a.max(), b) == 3
assert compute(t.a.mean(), b) == 2.0
assert abs(compute(t.a.std(), b) - np.std([1, 2, 3])) < 1e-5
assert abs(compute(t.a.var(), b) - np.var([1, 2, 3])) < 1e-5
assert abs(compute(t.a.std(unbiased=True), b) - np.std([1, 2, 3],
ddof=1)) < 1e-5
assert abs(compute(t.a.var(unbiased=True), b) - np.var([1, 2, 3],
ddof=1)) < 1e-5
assert len(list(compute(t.distinct(), b))) == 3
assert len(list(compute(t.a.distinct(), b))) == 3
assert compute(t.a.nunique(), b) == 3
assert isinstance(compute(t.a.nunique(), b), np.integer)
assert compute(t.a.count(), b) == 3
assert isinstance(compute(t.date.count(), b), np.integer)
assert compute(t.date.nunique(), b) == 2
assert isinstance(compute(t.date.nunique(), b), np.integer)
assert compute(t.date.count(), b) == 2
assert isinstance(compute(t.a.count(), b), np.integer)
assert compute(t.a[0], b) == 1
assert compute(t.a[-1], b) == 3
assert compute(t[0], b) == compute(t[0], b)
assert compute(t[-1], b) == compute(t[-1], b)
def _get_likelihood(self, model):
"""Compute the marginal likelihood of the linear model with a g-prior on betas.
Parameters
----------
model : np.ndarray in R^ndim
vector of variable inclusion indicators
Returns
-------
float
log marginal likelihood
"""
X = self.X[:, model == 1]
y = self.y
nobs, ndim = X.shape
design = np.hstack((np.ones((nobs, 1)), X))
mle = np.linalg.solve(np.dot(design.T, design), np.dot(design.T, y))
residuals = y - np.dot(design, mle)
rsquared = 1 - np.var(residuals) / np.var(y)
return (log_gamma((nobs - 1) / 2)
- (nobs - 1) / 2 * np.log(np.pi)
- 0.5 * np.log(nobs)
- (nobs - 1) / 2 * np.log(np.dot(residuals, residuals))
+ (nobs - ndim - 1) / 2 * np.log(1 + self.par["penalty"])
- (nobs - 1) / 2 * np.log(1 + self.par["penalty"] * (1 - rsquared)))
def welch_ttest (X, y):
classes = np.unique(y)
n_class = len(classes)
n_feats = X.shape[1]
b = np.zeros(n_feats)
for i in np.arange(n_class):
for j in np.arange(i+1, n_class):
if j > i:
xi = X[y == i, :]
xj = X[y == j, :]
yi = y[y == i]
yj = y[y == j]
mi = np.mean (xi, axis=0)
mj = np.mean (xj, axis=0)
vi = np.var (xi, axis=0)
vj = np.var (xj, axis=0)
n_subjsi = len(yi)
n_subjsj = len(yj)
t = (mi - mj) / np.sqrt((np.square(vi) / n_subjsi) + (np.square(vj) / n_subjsj))
t[np.isnan(t)] = 0
t[np.isinf(t)] = 0
b = np.maximum(b, t)
return b
def curv_fit(x=None, y=None, model=None):
x = np.array(x)
y = np.array(y)
params = lmfit.Parameters()
if model == 'gaussian':
mod = lmfit.models.GaussianModel()
params = mod.guess(y, x=x)
out = mod.fit(y,params, x=x)
r_sq = 1 - out.residual.var()/np.var(y)
elif model == '4PL':
mod = lmfit.Model(logistic_4p)
params.add('la', value=1.0)
params.add('gr', value=120.0, vary=False)
params.add('ce', value=150.0)
params.add('ua', value=3.0)
out = mod.fit(y, params,x=x)
r_sq = 1 - out.residual.var()/np.var(y)
elif model == '5PL':
mod = lmfit.Model(logistic_5p)
params.add('la', value=1.0)
params.add('gr', value=1.0)
params.add('ce', value=1.0)
params.add('ua', value=1.0)
params.add('sy', value=1.0)
out = mod.fit(y, params, x=x)
r_sq = 1 - out.residual.var()/np.var(y)
out.R_sq = r_sq
return out
def explained_variance_score(y_true, y_pred):
"""Explained variance regression score function
Best possible score is 1.0, lower values are worse.
Note: the explained variance is not a symmetric function.
return the explained variance
Parameters
----------
y_true : array-like
y_pred : array-like
"""
y_true, y_pred = check_arrays(y_true, y_pred)
numerator = np.var(y_true - y_pred)
denominator = np.var(y_true)
if denominator == 0.0:
if numerator == 0.0:
return 1.0
else:
# arbitary set to zero to avoid -inf scores, having a constant
# y_true is not interesting for scoring a regression anyway
return 0.0
return 1 - numerator / denominator
def bhattacharyya_dist (X, y):
classes = np.unique(y)
n_class = len(classes)
n_feats = X.shape[1]
b = np.zeros(n_feats)
for i in np.arange(n_class):
for j in np.arange(i+1, n_class):
if j > i:
xi = X[y == i, :]
xj = X[y == j, :]
mi = np.mean (xi, axis=0)
mj = np.mean (xj, axis=0)
vi = np.var (xi, axis=0)
vj = np.var (xj, axis=0)
si = np.std (xi, axis=0)
sj = np.std (xj, axis=0)
d = 0.25 * (np.square(mi - mj) / (vi + vj)) + 0.5 * (np.log((vi + vj) / (2*si*sj)))
d[np.isnan(d)] = 0
d[np.isinf(d)] = 0
b = np.maximum(b, d)
return b
def indivConfInter(self, data):
if type(data) is float:
med = numpy.median(data)
mean = numpy.mean(data)
stdDev = math.sqrt(numpy.var(data))
#confidence interval
ci95low = mean - 10 * (1.96 * stdDev)
ci95up = mean + 10 * (1.96 * stdDev)
#confidence level
cl95low = med - (1.96 * stdDev)
cl95up = med + (1.96 * stdDev)
return [med, mean, ci95low, ci95up, cl95low, cl95up]
elif len(data) > 0:
med = numpy.median(data)
mean = numpy.mean(data)
stdDev = math.sqrt(numpy.var(data))
ci95low = mean - 10 * (1.96 * (stdDev / math.sqrt(len(data))))
ci95up = mean + 10 * (1.96 * (stdDev / math.sqrt(len(data))))
cl95low = med - (1.96 * (stdDev / math.sqrt(len(data))))
cl95up = med + (1.96 * (stdDev / math.sqrt(len(data))))
return [med, mean, ci95low, ci95up, cl95low, cl95up]
else:
return [None, None, None, None, None, None]
def calc_twosample_ts(propGroup1, propGroup2):
n1 = len(propGroup1[0])
n2 = len(propGroup2[0])
numFeatures = len(propGroup1)
T_statistics = []
effectSizes = []
notes = []
for r in xrange(0, numFeatures):
meanG1 = float(sum(propGroup1[r])) / n1
varG1 = var(propGroup1[r], ddof=1)
stdErrG1 = varG1 / n1
meanG2 = float(sum(propGroup2[r])) / n2
varG2 = var(propGroup2[r], ddof=1)
stdErrG2 = varG2 / n2
dp = meanG1 - meanG2
effectSizes.append(dp * 100)
denom = math.sqrt(stdErrG1 + stdErrG2)
if denom == 0:
notes.append("degenerate case: zero variance for both groups; variance set to 1e-6.")
T_statistics.append(dp / 1e-6)
else:
notes.append("")
T_statistics.append(dp / denom)
return T_statistics, effectSizes, notes
def drawPlot(m):
print("Poly degree is %d" % m)
A = getAMatrix(x,m,sigma)
B = getBColumn(x,y,m,sigma)
#print("A matrix is")
#print(A)
#print("B column is")
#print(B)
c,v = solveKramer(A, B)
polyCurve = np.poly1d(c)
ty = polyCurve(x) #teoretic y
print("calculated coeficients are:")
print(c)
print("coef variance:")
print(v)
#polyfit throws this error sometimes: when rank(A)< m(equiv A cannot be inversed and there is no unique solution)?
try:
polyC, polyV = np.polyfit(x,y,m, full=False, cov=True)
print("coef from polyfit are:")
print(polyC)
print("covariance matrix from polyfit are:")
print(polyV)
except ValueError as err:
print("Error in numpy.polyfit " )
print(err)
print("-------------------------------------------------------------------")
print("goodness=R**2=%4.3f"% (1 - np.var(np.subtract(ty,y)) / np.var(y)) )
#corrected division by 0 error when m==n
print("avg unc=sigma**2=%4.3f"% ((1.0 / ((n-m) if n!=m else 1) ) * np.sum(np.power(np.subtract(ty,y),2))) )
l.set_ydata(ty)
ax.relim()
ax.autoscale_view(True,True,True)
plt.draw()
def fig_spesen(spe, sen, fname='fig_model.png', leg=False):
"""Plot the specificity for the early, middle and end section"""
fig, ax = plt.subplots(figsize=(3, 3))
fa_rate = 1 - np.mean(spe, axis=0)
fa_err = np.var(spe, axis=0)
hits_rate = np.mean(sen, axis=0)
hits_err = np.var(sen, axis=0)
ax.plot(np.arange(1, 4), hits_rate, color='#47bb3a', linewidth=1)
ax.plot(np.arange(1, 4), fa_rate, color='#ec1d2a', linewidth=1)
width = 0.5
ax.bar(np.arange(1, 4) - width/2., hits_rate,
width, yerr=hits_err, color='#a7db60', label='HIT rate',
error_kw=dict(ecolor='black', lw=1, capsize=2.5, capthick=1))
ax.bar(np.arange(1, 4) - width/2., fa_rate,
width, yerr=fa_err, color='#f2507b', label='FA rate',
error_kw=dict(ecolor='black', lw=1, capsize=2.5, capthick=1))
plt.xlim(0, 4)
plt.ylim(0, 0.9)
plt.ylabel("Response Rate")
plt.xlabel(r'Session Start $\rightarrow$ Session End')
ax.set_xticks(range(1, 4))
ax.set_xticklabels(["Initial", "Middle", "Final"], fontsize=10)
colors = ("red", "orange", "green")
[t.set_color(colors[i]) for i, t in enumerate(plt.gca().get_xticklabels())]
adjust_spines(ax, ["bottom", "left"])
if leg:
ax.legend(fontsize=10)
plt.tight_layout()
plt.savefig(fname)
def test_pairwise_distances_data_derived_params(n_jobs, metric, dist_function,
y_is_x):
# check that pairwise_distances give the same result in sequential and
# parallel, when metric has data-derived parameters.
with config_context(working_memory=1): # to have more than 1 chunk
rng = np.random.RandomState(0)
X = rng.random_sample((1000, 10))
if y_is_x:
Y = X
expected_dist_default_params = squareform(pdist(X, metric=metric))
if metric == "seuclidean":
params = {'V': np.var(X, axis=0, ddof=1)}
else:
params = {'VI': np.linalg.inv(np.cov(X.T)).T}
else:
Y = rng.random_sample((1000, 10))
expected_dist_default_params = cdist(X, Y, metric=metric)
if metric == "seuclidean":
params = {'V': np.var(np.vstack([X, Y]), axis=0, ddof=1)}
else:
params = {'VI': np.linalg.inv(np.cov(np.vstack([X, Y]).T)).T}
expected_dist_explicit_params = cdist(X, Y, metric=metric, **params)
dist = np.vstack(tuple(dist_function(X, Y,
metric=metric, n_jobs=n_jobs)))
assert_allclose(dist, expected_dist_explicit_params)
assert_allclose(dist, expected_dist_default_params)
def statprint(host_per_pg, pg_per_host):
val = pg_per_host.values() # sets val to a list of the values in pg_per_host
mean = numpy.mean(val)
maxvalue = numpy.amax(val)
minvalue = numpy.amin(val)
std = numpy.std(val)
median = numpy.median(val)
variance = numpy.var(val)
print("for placement groups on hosts: ")
print( "the mean is: ", mean)
print( "the max value is: ", maxvalue)
print( "the min value is: ", minvalue)
print( "the standard deviation is: ", std)
print( "the median is: ", median)
print( "the variance is: ", variance)
# prints statements for stats
host_mean = numpy.mean(host_per_pg)
host_max = numpy.amax(host_per_pg)
host_min = numpy.amin(host_per_pg)
host_std = numpy.std(host_per_pg)
host_median = numpy.median(host_per_pg)
host_variance = numpy.var(host_per_pg)
# these are the variables for hosts/pgs
print("hosts per placement group: ")
print("the mean is: ", host_mean)
print("the max value is: ", host_max)
print("the min value is: ", host_min)
print("the standard deviation is: ", host_std)
print("the median is: ", host_median)
print("the variance is: ", host_variance)
def XDapogee(options,args):
#First load the chains
savefile= open(args[0],'rb')
thesesamples= pickle.load(savefile)
savefile.close()
vcs= numpy.array([s[0] for s in thesesamples])*_APOGEEREFV0/_REFV0
dvcdrs= numpy.array([s[6] for s in thesesamples])*30. #To be consistent with this project's dlnvcdlnr
print numpy.mean(vcs)
print numpy.mean(dvcdrs)
#Now fit XD to the 2D PDFs
ydata= numpy.zeros((len(vcs),2))
ycovar= numpy.zeros((len(vcs),2))
ydata[:,0]= numpy.log(vcs)
ydata[:,1]= dvcdrs
vcxamp= numpy.ones(options.g)/options.g
vcxmean= numpy.zeros((options.g,2))
vcxcovar= numpy.zeros((options.g,2,2))
for ii in range(options.g):
vcxmean[ii,:]= numpy.mean(ydata,axis=0)+numpy.std(ydata,axis=0)*numpy.random.normal(size=(2))/4.
vcxcovar[ii,0,0]= numpy.var(ydata[:,0])
vcxcovar[ii,1,1]= numpy.var(ydata[:,1])
extreme_deconvolution.extreme_deconvolution(ydata,ycovar,
vcxamp,vcxmean,vcxcovar)
save_pickles(options.plotfile,
vcxamp,vcxmean,vcxcovar)
print vcxamp
print vcxmean[:,0]
print vcxmean[:,1]
return None
def AsianCallSimPrice(S0, K, T, r, sigma, M, I, CV=False):
dt = T / M
S = np.zeros((M + 1, I))
z = np.random.standard_normal((M + 1, I)) # pseudorandom numbers
Savg = np.zeros(I)
S[0] = S0
S = S0 * np.exp(np.cumsum((r - 0.5 * sigma ** 2) * dt + sigma * np.sqrt(dt) * z, axis=0))
Savg = np.average(S, axis=0)
if CV == False:
price = np.exp(-r * T) * np.sum(np.maximum(Savg - K, 0)) / I
error = math.sqrt(np.var(np.maximum(Savg - K, 0))) / math.sqrt(I)
result = (price, error)
else:
Tvector = np.arange(dt, T + dt, dt)
T_avg = Tvector.mean()
i_vector = np.arange(1, 2 * M + 1, 2)
sigma_avg = math.sqrt(sigma ** 2 / (M ** 2 * T_avg) * np.dot(i_vector, Tvector[::-1]))
delta = 0.5 * (sigma ** 2 - sigma_avg ** 2)
d = (math.log(S0 / K) + (r - delta + 0.5 * sigma_avg ** 2) * T_avg) / (sigma_avg * math.sqrt(T_avg))
GeomAsianCall = np.exp(-delta * T_avg) * S0 * scipy.stats.norm.cdf(d) - np.exp(
-r * T_avg
) * K * scipy.stats.norm.cdf(d - sigma_avg * math.sqrt(T_avg))
S_CV = scipy.stats.mstats.gmean(S, axis=0)
X = np.exp(-r * T) * np.maximum(S_CV - K, 0)
Y = np.exp(-r * T) * np.maximum(Savg - K, 0)
b = np.cov(X, Y)[0][1] / X.var()
price = Y.mean() - b * (X.mean() - GeomAsianCall)
error = math.sqrt(np.var(Y - b * X)) / math.sqrt(I)
rho = np.corrcoef(X, Y)[0][1]
result = (price, error, rho)
return result
def log_evidence(X, y, g):
"""Compute the model's log evidence (a.k.a. marginal likelihood).
Parameters
----------
X : np.ndarray in R^(nobs x ndim)
feature matrix
y : np.ndarray in R^nobs
target vector
g : float (0, inf)
dimensionality penalty
Returns
-------
float
log evidence
"""
n, d = X.shape
X_int = np.hstack((np.ones((n, 1)), X))
mle = np.linalg.solve(np.dot(X_int.T, X_int), np.dot(X_int.T, y))
resid = y - np.dot(X_int, mle)
rsq = (d > 0 and 1 - np.var(resid) / np.var(y)) or 0
return (log_gamma((n - 1) / 2)
- (n - 1) / 2 * np.log(np.pi)
- 0.5 * np.log(n)
- (n - 1) / 2 * np.log(np.dot(resid, resid))
+ (n - d - 1) / 2 * np.log(1 + 1 / g)
- (n - 1) / 2 * np.log(1 + 1 / g * (1 - rsq)))
def main():
images, labels = load_labeled_training(flatten=True)
images = standardize(images)
unl = load_unlabeled_training(flatten=True)
unl = standardize(unl)
test = load_public_test(flatten=True)
test = standardize(test)
shuffle_in_unison(images, labels)
#d = DictionaryLearning().fit(images)
d = MiniBatchDictionaryLearning(n_components=500, n_iter=500, verbose=True).fit(images)
s = SparseCoder(d.components_)
proj_test = s.transform(images)
pt = s.transform(test)
#kpca = KernelPCA(kernel="rbf")
#kpca.fit(unl)
#test_proj = kpca.transform(images)
#pt = kpca.transform(test)
#spca = SparsePCA().fit(unl)
#test_proj = spca.transform(images)
#pt = spca.transform(test)
svc = SVC()
scores = cross_validation.cross_val_score(svc, proj_test, labels, cv=10)
print scores
print np.mean(scores)
print np.var(scores)
svc.fit(proj_test, labels)
pred = svc.predict(pt)
write_results(pred, '../svm_res.csv')
def findvdisp3(self,r,v,mags,r200,maxv): "use red sequence to find members" binedge = np.arange(0,r200+1,0.3) rin = r vin = v colin = mags.T[1] - mags.T[2] avg_c = np.average(colin) vfinal = np.array([]) for i in range(binedge.size-1): i += 1 x = rin[np.where((rin>binedge[i-1]) & (rin<binedge[i]))] y = vin[np.where((rin>binedge[i-1]) & (rin<binedge[i]))] c = colin[np.where((rin>binedge[i-1]) & (rin<binedge[i]))] for k in range(6): y2 = y x2 = x c2 = c stv = 3.5 * np.std(y2) y = y2[np.where((y2 > -stv) & (y2 < stv) | ((c2<avg_c+0.04) & (c2>avg_c-0.04)))] x = x2[np.where((y2 > -stv) & (y2 < stv) | ((c2<avg_c+0.04) & (c2>avg_c-0.04)))] c = c2[np.where((y2 > -stv) & (y2 < stv) | ((c2<avg_c+0.04) & (c2>avg_c-0.04)))] vstd2 = np.std(y) vvar2 = np.var(y) print 'standard dev of zone %i = %f' % (i,vstd2) vfinal = np.append(y[np.where((y<vvar2) & (y>-vvar2))],vfinal) return np.var(vfinal)
def classify_2d(data_a, data_b, x):
x1 = x[0]
x2 = x[1]
probability_a = data_a.shape[1] / (data_a.shape[1] + data_b.shape[1])
probability_b = data_b.shape[1] / (data_a.shape[1] + data_b.shape[1])
mean_x1_a = np.mean(data_a[0,:])
mean_x2_a = np.mean(data_a[1,:])
mean_x1_b = np.mean(data_b[0,:])
mean_x2_b = np.mean(data_b[1,:])
variance_x1_a = np.var(data_a[0,:])
variance_x2_a = np.var(data_a[1,:])
variance_x1_b = np.var(data_b[0,:])
variance_x2_b = np.var(data_b[1,:])
pd_x1_given_a = mlab.normpdf(x1, mean_x1_a, variance_x1_a)
pd_x2_given_a = mlab.normpdf(x2, mean_x2_a, variance_x2_a)
pd_x1_given_b = mlab.normpdf(x1, mean_x1_b, variance_x1_b)
pd_x2_given_b = mlab.normpdf(x2, mean_x2_b, variance_x2_b)
posterior_numerator_a = probability_a * pd_x1_given_a * pd_x2_given_a
posterior_numerator_b = probability_b * pd_x1_given_b * pd_x2_given_b
posterior_numerators = { 'A': posterior_numerator_a, 'B': posterior_numerator_b }
return max(posterior_numerators.iterkeys(), key=(lambda k: posterior_numerators[k]))
def calc_com(mask):
pts = index_to_zyx( mask )
z = pts[0,:].astype(float).mean()
# Correct Center of Mass for reentrant domain
y1 = pts[1,:].astype(float)
x1 = pts[2,:].astype(float)
y2 = (y1 < ny/2.)*y1 + (y1>= ny/2.)*(y1 - ny)
x2 = (x1 < nx/2.)*x1 + (x1>= nx/2.)*(x1 - nx)
y1m = y1.mean()
y2m = y2.mean()
x1m = x1.mean()
x2m = x2.mean()
if numpy.var(y2 - y2m) > numpy.var(y1 - y1m):
y = y1m
else:
y = (y2m + .5)%ny - .5
if numpy.var(x2 - x2m) > numpy.var(x1 - x1m):
x = x1m
else:
x = (x2m + .5)%nx - .5
return numpy.array((z, y, x))
def _tTest(x, y, exclude=95):
"""Compute a one-sided Welsh t-statistic."""
with np.errstate(all="ignore"):
def cappedSlog(v):
q = np.percentile(v, exclude)
v2 = v.copy()
v2 = v2[~np.isnan(v2)]
v2[v2 > q] = q
v2[v2 <= 0] = 1. / (75 + 1)
return np.log(v2)
x1 = cappedSlog(x)
x2 = cappedSlog(y)
sx1 = np.var(x1) / len(x1)
sx2 = np.var(x2) / len(x2)
totalSE = np.sqrt(sx1 + sx2)
if totalSE == 0:
stat = 0
else:
stat = (np.mean(x1) - np.mean(x2)) / totalSE
#df = (sx1 + sx2)**2 / (sx1**2/(len(x1)-1) + sx2**2/(len(x2) - 1))
#pval = 1 - scidist.t.cdf(stat, df)
# Scipy's t distribution CDF implementaton has inadequate
# precision. We have switched to the normal distribution for
# better behaved p values.
pval = 0.5 * erfc(stat / sqrt(2))
return {'testStatistic': stat, 'pvalue': pval}
def calc_error(data):
"""
Error estimation for time series of simulation observables and take into
account that these series are to some kind degree correlated (which
enhances the estimated statistical error).
"""
# calculate the normalized autocorrelation function of data
acf = autocorrelation(data)
# calculate the integrated correlation time tau_int
# (Janke, Wolfhard. "Statistical analysis of simulations: Data correlations
# and error estimation." Quantum Simulations of Complex Many-Body Systems:
# From Theory to Algorithms 10 (2002): 423-445.)
tau_int = 0.5
for i in range(len(acf)):
tau_int += acf[i]
if ( i >= 6 * tau_int ):
break
# mean value of the time series
data_mean = np.mean(data)
# calculate the so called effective length of the time series N_eff
if (tau_int > 0.5):
N_eff = len(data) / (2.0 * tau_int)
# finally the error is sqrt(var(data)/N_eff)
stat_err = np.sqrt(np.var(data) / N_eff)
else:
stat_err = np.sqrt(np.var(data) / len(data))
return data_mean, stat_err
sys.getsizeof(graph_data[1].Ri) +
sys.getsizeof(graph_data[1].Ro) +
sys.getsizeof(graph_data[1].y) +
sys.getsizeof(graph_data[1].a))/10.**6)
n_nodes, n_edges = get_shape(graph_data)
node_counts.append(n_nodes)
edge_counts.append(n_edges)
truth_eff = get_truth_efficiency(i, graph_data, truth_table)
seg_eff = get_segment_efficiency(graph_data)
truth_effs.append(truth_eff)
seg_effs.append(seg_eff)
avg_seg_eff = [np.mean(seg_effs), np.sqrt(np.var(seg_effs))]
avg_truth_eff = [np.mean(truth_effs), np.sqrt(np.var(truth_effs))]
avg_nodes = [np.mean(node_counts), np.sqrt(np.var(node_counts))]
avg_edges = [np.mean(edge_counts), np.sqrt(np.var(edge_counts))]
avg_size = [np.mean(sizes), np.sqrt(np.var(sizes))]
# print out a brief report of the measurements
data_tag = " ***** pt=" + pt_cuts[i] + " data ***** "
print("{0}\n \t seg_eff: {1} +/- {2} \n \t truth_eff: {3} +/- {4}"
.format(data_tag, np.round(avg_seg_eff[0], decimals=3),
np.round(avg_seg_eff[1], decimals=3),
np.round(avg_truth_eff[0], decimals=3),
np.round(avg_truth_eff[1], decimals=3)))
print("\t nodes: {0} +/- {1} \n \t edges: {2} +/- {3}"
.format(np.round(avg_nodes[0], decimals=3),
frame = pyfits.getdata(dir+filename_sub+str(files[i])+'.fits')
cds = fluxdir*(frame-f9)
#Define reference pixels
ref = [0,1,2,3, len(cds)-1, len(cds)-2, len(cds)-3, len(cds)-4]
#subtract median of reference pixels
for j in range((cds.shape)[1]):
cds[ : , j] -= np.median(cds[ref, j])
for j in range(n_crops):
crop = cds[center+yshifts[j] : center+yshifts[j]+70 ,
center+1+(j-n_crops/2)*64 : center-1+(j-n_crops/2)*64 +64 ]
#crosstalk correction increases the variance but does not affect the signal
mask = masks[j*2:j*2+2]
noisearr[i , j] = np.var(crop[mask], ddof=1) * correction
fluxarr[i , j] = np.mean(crop[mask])
#verify masking is being reasonable
if i == len(files)-1:
myrange = [np.median(crop)-10.*np.std(crop[mask]) ,
np.median(crop)+10.*np.std(crop[mask])]
junk = plt.hist(crop.flatten(), bins=50, range=myrange)
plt.title("Pixel Brightness Histogram, CDS "+str(max(files))+'-09')
plt.xlabel('ADU')
plt.ylabel('N_Occurences')
plt.vlines( (np.min(crop[mask]) , np.max(crop[mask])) ,
0.1, 1.2*np.max(junk[0]), colors='r')
plt.show()
if j > 11 and j < (length - 1):
data = line.split(",")
#print ("J= ", j)
interface = data[2]
#print ("interafce= ", interface)
inter = interface.split("/")
#print (inter[0], inter[1], inter[2])
if data[3] == "6\n" and inter[2] == str(i):
#print (data[0], data[1], data[2], data[3])
portno = int(data[1])
#print ("port no type = ", type(portno))
#print ("port no = ", portno)
port.insert(k, portno)
k = k + 1
portlen = len(port)
port.sort()
if portlen > 0:
max = port[portlen - 1]
min = port[0]
avg = math.ceil(numpy.mean(port))
var = math.ceil(numpy.var(port))
std = math.ceil(numpy.std(port))
print("### interface= G1/0/", i, "tcp port numbers= ", portlen,
"gap= ", max - min, "max= ", max, "min =", min, "avg= ", avg,
"var= ", var, "std= ", std, "tcp ports are= ", port)
min_res = min(res)
inversas = []
for value in res:
k = int(math.floor((value - delta) / w)) # Este es mi indice de bin
#interpola los valores de la F^-1(x)
inv = ((value - (delta + k * w)) / w) * (x[k + 1] - x[k]) + x[k]
#inv = np.interp(value,[delta+k*w, delta+(k+1)*w],[x[k],x[k+1]])
inversas.append(inv)
#print inversas
max_inv = max(inversas)
min_inv = min(inversas)
#h = [0] * m
#for value in inversas:
# k = int(math.floor((value+gamma)/q))
# h[k] = h[k]+1
#print sum(h)
#p, bins, patches = plt.hist(inversas, m , density=True, facecolor='g', alpha=0.75)
inversas_2 = []
for i in inversas:
inversas_2.append(round(i, 2))
print "Media Calculada: " + str(np.mean(inversas_2)) + " vs " + "0"
print "Desvio Calculado: " + str(np.var(inversas_2)) + " vs " + "1"
print "Moda Calculada: " + str(stats.mode(inversas_2)[0]) + " vs " + "0"
plt.hist(inversas_2, 50)
plt.show()
print(np.mean(a2, axis = 0))
print(np.mean(a2, axis = 1))
print()
# std() : 표준 편차 계산
print('std() : 표준 편차 계산')
print(a2)
print(np.std(a2))
print(np.std(a2, axis = 0))
print(np.std(a2, axis = 1))
print()
# var() : 분산 계산
print('var() : 분산 계산')
print(a2)
print(np.var(a2))
print(np.var(a2, axis = 0))
print(np.var(a2, axis = 1))
print()
# min() : 최소값
print('min() : 최소값')
print(a2)
print(np.min(a2))
print(np.min(a2, axis = 0))
print(np.min(a2, axis = 1))
print()
# max() : 최대값
print('max() : 최대값')
print(a2)
Y = np.array([f(V2X(V[i,:])) for i in range(ned)])
# reformat if necessary
if np.size(np.shape(Y))==1:
Y = np.expand_dims(Y, axis=1)
# build surrogates
# fpce,_ = MM.build_fpce(V, Y, ppce)
fpce = MM.build_spce(V, Y, Q, ppce, q)
flra,_,_ = MM.build_cp(V, Y, R, plra)
# compute generalization error (MC estimate)
Ypce = np.array([fpce(Ve[i,:]) for i in range(nmc)]).flatten()
Ylra = np.array([flra(Ve[i,:]) for i in range(nmc)]).flatten()
err_pce = np.mean((Ye - Ypce)**2)/np.var(Ye)
err_lra = np.mean((Ye - Ylra)**2)/np.var(Ye)
print "\nGeneralization Error Comparison\n\n"\
"PCE: g_e = {0}\n"\
"LRA: g_e = {1}".format(err_pce,err_lra)
# ---------------------------------------------------------------------------
# density plots
# ---------------------------------------------------------------------------
de = ss.kde.gaussian_kde(np.abs(Ye))
dpce = ss.kde.gaussian_kde(np.abs(Ypce))
dlra = ss.kde.gaussian_kde(np.abs(Ylra))
y = np.linspace(min(np.abs(Ye)),max(np.abs(Ye)),100)
def var(x):
return np.var(x, ddof=1)
def processData(self):
self.NcascStart = self.par_obj.NcascStart
self.NcascEnd = self.par_obj.NcascEnd
self.Nsub = self.par_obj.Nsub
self.winInt = self.par_obj.winInt
#self.subChanArr, self.trueTimeArr, self.dTimeArr,self.resolution = pt3import(self.filepath)
if self.ext == 'pt2':
self.subChanArr, self.trueTimeArr, self.dTimeArr, self.resolution = pt2import(
self.filepath)
if self.ext == 'pt3':
self.subChanArr, self.trueTimeArr, self.dTimeArr, self.resolution = pt3import(
self.filepath)
if self.ext == 'csv':
self.subChanArr, self.trueTimeArr, self.dTimeArr, self.resolution = csvimport(
self.filepath)
if self.ext == 'spc':
self.subChanArr, self.trueTimeArr, self.dTimeArr, self.resolution = spc_file_import(
self.filepath)
if self.ext == 'asc':
self.subChanArr, self.trueTimeArr, self.dTimeArr, self.resolution = asc_file_import(
self.filepath)
self.subArrayGeneration(self.xmin, self.xmax)
self.dTimeMin = self.parentId.dTimeMin
self.dTimeMax = self.parentId.dTimeMax
self.subDTimeMin = self.dTimeMin
self.subDTimeMax = self.dTimeMax
#Time series of photon counts. For visualisation.
self.timeSeries1, self.timeSeriesScale1 = delayTime2bin(
np.array(self.trueTimeArr) / 1000000, np.array(self.subChanArr),
self.ch_present[0], self.photonCountBin)
unit = self.timeSeriesScale1[-1] / self.timeSeriesScale1.__len__()
self.kcount_CH1 = np.average(self.timeSeries1)
raw_count = np.average(
self.timeSeries1
) #This is the unnormalised intensity count for int_time duration (the first moment)
var_count = np.var(self.timeSeries1)
self.brightnessNandBCH0 = (((var_count - raw_count) / (raw_count)) /
(float(unit)))
if (var_count - raw_count) == 0:
self.numberNandBCH0 = 0
else:
self.numberNandBCH0 = (raw_count**2 / (var_count - raw_count))
if self.numOfCH == 2:
self.timeSeries2, self.timeSeriesScale2 = delayTime2bin(
np.array(self.trueTimeArr) / 1000000,
np.array(self.subChanArr), self.ch_present[1],
self.photonCountBin)
unit = self.timeSeriesScale2[-1] / self.timeSeriesScale2.__len__()
self.kcount_CH2 = np.average(self.timeSeries2)
raw_count = np.average(
self.timeSeries2
) #This is the unnormalised intensity count for int_time duration (the first moment)
var_count = np.var(self.timeSeries2)
self.brightnessNandBCH1 = (((var_count - raw_count) /
(raw_count)) / (float(unit)))
if (var_count - raw_count) == 0:
self.numberNandBCH1 = 0
else:
self.numberNandBCH1 = (raw_count**2 / (var_count - raw_count))
self.CV = calc_coincidence_value(self)
#Adds names to the fit function for later fitting.
if self.objId1 == None:
corrObj = corrObject(self.filepath, self.fit_obj)
self.objId1 = corrObj.objId
self.objId1.parent_name = 'pt FCS tgated -tg0: ' + str(
np.round(self.xmin, 0)) + ' -tg1: ' + str(
np.round(self.xmax, 0))
self.objId1.parent_uqid = 'pt FCS tgated -tg0: ' + str(
np.round(self.xmin, 0)) + ' -tg1: ' + str(
np.round(self.xmax, 0))
self.fit_obj.objIdArr.append(corrObj.objId)
self.objId1.param = copy.deepcopy(self.fit_obj.def_param)
self.objId1.name = self.name + '_CH0_Auto_Corr'
self.objId1.ch_type = 0 #channel 0 Auto
self.objId1.siblings = None
self.objId1.prepare_for_fit()
self.objId1.kcount = self.kcount_CH1
self.objId1.autoNorm = np.array(self.autoNorm[:, 0, 0]).reshape(-1)
self.objId1.autotime = np.array(self.autotime).reshape(-1)
self.objId1.param = copy.deepcopy(self.fit_obj.def_param)
if self.numOfCH == 2:
self.objId1.CV = self.CV
if self.objId3 == None:
corrObj = corrObject(self.filepath, self.fit_obj)
self.objId3 = corrObj.objId
self.objId3.parent_name = 'pt FCS tgated -tg0: ' + str(
np.round(self.xmin, 0)) + ' -tg1: ' + str(
np.round(self.xmax, 0))
self.objId3.parent_uqid = 'pt FCS tgated -tg0: ' + str(
np.round(self.xmin, 0)) + ' -tg1: ' + str(
np.round(self.xmax, 0))
self.fit_obj.objIdArr.append(corrObj.objId)
self.objId3.param = copy.deepcopy(self.fit_obj.def_param)
self.objId3.name = self.name + '_CH1_Auto_Corr'
self.objId3.ch_type = 1 #channel 1 Auto
self.objId3.siblings = None
self.objId3.prepare_for_fit()
self.objId3.kcount = self.kcount_CH2
self.objId3.autoNorm = np.array(self.autoNorm[:, 1, 1]).reshape(-1)
self.objId3.autotime = np.array(self.autotime).reshape(-1)
self.objId3.param = copy.deepcopy(self.fit_obj.def_param)
self.objId3.CV = self.CV
if self.objId2 == None:
corrObj = corrObject(self.filepath, self.fit_obj)
self.objId2 = corrObj.objId
self.objId2.parent_name = 'pt FCS tgated -tg0: ' + str(
np.round(self.xmin, 0)) + ' -tg1: ' + str(
np.round(self.xmax, 0))
self.objId2.parent_uqid = 'pt FCS tgated -tg0: ' + str(
np.round(self.xmin, 0)) + ' -tg1: ' + str(
np.round(self.xmax, 0))
self.objId2.param = copy.deepcopy(self.fit_obj.def_param)
self.fit_obj.objIdArr.append(corrObj.objId)
self.objId2.name = self.name + '_CH01_Cross_Corr'
self.objId2.ch_type = 2 #channel 01 Cross
self.objId2.siblings = None
self.objId2.prepare_for_fit()
self.objId2.autoNorm = np.array(self.autoNorm[:, 0, 1]).reshape(-1)
self.objId2.autotime = np.array(self.autotime).reshape(-1)
self.objId2.param = copy.deepcopy(self.fit_obj.def_param)
self.objId2.CV = self.CV
if self.objId4 == None:
corrObj = corrObject(self.filepath, self.fit_obj)
self.objId4 = corrObj.objId
self.objId4.parent_name = 'pt FCS tgated -tg0: ' + str(
np.round(self.xmin, 0)) + ' -tg1: ' + str(
np.round(self.xmax, 0))
self.objId4.parent_uqid = 'pt FCS tgated -tg0: ' + str(
np.round(self.xmin, 0)) + ' -tg1: ' + str(
np.round(self.xmax, 0))
self.objId4.param = copy.deepcopy(self.fit_obj.def_param)
self.fit_obj.objIdArr.append(corrObj.objId)
self.objId4.name = self.name + '_CH10_Cross_Corr'
self.objId4.ch_type = 3 #channel 10 Cross
self.objId4.siblings = None
self.objId4.prepare_for_fit()
self.objId4.autoNorm = np.array(self.autoNorm[:, 1, 0]).reshape(-1)
self.objId4.autotime = np.array(self.autotime).reshape(-1)
self.objId4.CV = self.CV
self.fit_obj.fill_series_list()
#del self.subChanArr
#self.trueTimeArr
del self.dTimeArr
def get_video_feat(video, feat_dates):
# 源数据
history = video[video['day'].map(lambda x: x in feat_dates)]
history['cnt'] = 1
# 返回的特征
feature = pd.DataFrame(columns=['user_id'])
## 统计特征
pivot = pd.pivot_table(history,
index=['user_id', 'day'],
values='cnt',
aggfunc=len)
pivot = pivot.unstack(level=-1)
pivot.fillna(0, downcast='infer', inplace=True)
feat = pd.DataFrame()
feat['user_id'] = pivot.index
feat.index = pivot.index
# 每一天的特征
for i in range(1, len(feat_dates) + 1):
feat['user_video_cnt_before_' + str(i) +
'_day'] = pivot[pivot.columns.tolist()[-i]]
# 总和
feat['user_video_cnt_sum'] = pivot.sum(1)
# 均值
feat['user_video_cnt_mean'] = pivot.mean(1)
# 方差
feat['user_video_cnt_var'] = pivot.var(1)
# 最大值
feat['user_video_cnt_max'] = pivot.max(1)
# 最小值
feat['user_video_cnt_min'] = pivot.min(1)
# 加入feature
feature = pd.merge(feature, feat, on=['user_id'], how='outer')
# ## 差分与统计
# diff = pivot.diff(axis = 1)
# diff = diff[diff.columns.tolist()[1:]]
# feat = pd.DataFrame()
# feat['user_id'] = diff.index
# feat.index = diff.index
# # 每一个差分
# for i in range(1,len(feat_dates)):
# feat['user_video_diff_before_' + str(i) + '_day'] = diff[diff.columns.tolist()[-i]]
# # 总和
# feat['user_video_diff_sum'] = diff.sum(1)
# # 均值
# feat['user_video_diff_mean'] = diff.mean(1)
# # 方差
# feat['user_video_diff_var'] = diff.var(1)
# # 最大值
# feat['user_video_diff_max'] = diff.max(1)
# # 最小值
# feat['user_video_diff_min'] = diff.min(1)
# # 加入feature
# feature = pd.merge(feature,feat,on = ['user_id'],how = 'outer')
## 连续拍摄
feat = pd.DataFrame()
feat['user_id'] = pivot.index
feat.index = pivot.index
pivot = pivot.applymap(lambda x: 1 if x != 0 else 0)
feat['video_list'] = pivot.apply(
lambda x: reduce(lambda y, z: str(y) + str(z), x), axis=1)
# 连续拍摄天数_均值
feat['user_video_continue_mean'] = feat['video_list'].map(
lambda x: np.mean([len(y) for y in re.split('0+', x.strip('0'))]))
# 连续拍摄天数_方差
feat['user_video_continue_var'] = feat['video_list'].map(
lambda x: np.var([len(y) for y in re.split('0+', x.strip('0'))]))
# 连续拍摄天数_最大值
feat['user_video_continue_max'] = feat['video_list'].map(
lambda x: np.max([len(y) for y in re.split('0+', x.strip('0'))]))
# 连续拍摄天数_最小值
feat['user_video_continue_min'] = feat['video_list'].map(
lambda x: np.min([len(y) for y in re.split('0+', x.strip('0'))]))
# 去掉无用的
feat.drop(['video_list'], axis=1, inplace=True)
# 加入feature
feature = pd.merge(feature, feat, on=['user_id'], how='outer')
## 时间间隔
# 最近/远一次拍摄距离最近考察日的时间间隔
near = 'nearest_day_video'
fur = 'furest_day_video'
pivot_n = pd.pivot_table(history,
index=['user_id'],
values='day',
aggfunc=max)
pivot_n.rename(columns={'day': near}, inplace=True)
pivot_n.reset_index(inplace=True)
pivot_f = pd.pivot_table(history,
index=['user_id'],
values='day',
aggfunc=min)
pivot_f.rename(columns={'day': fur}, inplace=True)
pivot_f.reset_index(inplace=True)
feature = pd.merge(feature, pivot_n, on=['user_id'], how='left')
feature = pd.merge(feature, pivot_f, on=['user_id'], how='left')
feature[near +
'_to_label'] = feature[near].map(lambda x: feat_dates[-1] + 1 - x)
feature[fur +
'_to_label'] = feature[fur].map(lambda x: feat_dates[-1] + 1 - x)
feature.drop([near, fur], axis=1, inplace=True)
## 填空
feature.fillna(0, downcast='infer', inplace=True)
## 返回
return feature
def processData(self):
self.NcascStart = self.par_obj.NcascStart
self.NcascEnd = self.par_obj.NcascEnd
self.Nsub = self.par_obj.Nsub
self.winInt = self.par_obj.winInt
self.photonCountBin = 25 #self.par_obj.photonCountBin
#File import
if self.ext == 'spc':
self.subChanArr, self.trueTimeArr, self.dTimeArr, self.resolution = spc_file_import(
self.filepath)
if self.ext == 'asc':
self.subChanArr, self.trueTimeArr, self.dTimeArr, self.resolution = asc_file_import(
self.filepath)
if self.ext == 'pt2':
self.subChanArr, self.trueTimeArr, self.dTimeArr, self.resolution = pt2import(
self.filepath)
if self.ext == 'pt3':
self.subChanArr, self.trueTimeArr, self.dTimeArr, self.resolution = pt3import(
self.filepath)
if self.ext == 'ptu':
out = ptuimport(self.filepath)
if out != False:
self.subChanArr, self.trueTimeArr, self.dTimeArr, self.resolution = out
else:
self.par_obj.data.pop(-1)
self.par_obj.objectRef.pop(-1)
self.exit = True
return
if self.ext == 'csv':
self.subChanArr, self.trueTimeArr, self.dTimeArr, self.resolution = csvimport(
self.filepath)
#If the file is empty.
if self.subChanArr == None:
#Undoes any preparation of resource.
self.par_obj.data.pop(-1)
self.par_obj.objectRef.pop(-1)
self.exit = True
return
#Colour assigned to file.
self.color = self.par_obj.colors[self.unqID % len(self.par_obj.colors)]
#How many channels there are in the files.
self.ch_present = np.sort(np.unique(np.array(self.subChanArr)))
if self.ext == 'pt3' or self.ext == 'ptu' or self.ext == 'pt2':
self.numOfCH = self.ch_present.__len__(
) - 1 #Minus 1 because not interested in channel 15.
else:
self.numOfCH = self.ch_present.__len__()
#Finds the numbers which address the channels.
#Calculates decay function for both channels.
self.photonDecayCh1, self.decayScale1 = delayTime2bin(
np.array(self.dTimeArr), np.array(self.subChanArr),
self.ch_present[0], self.winInt)
if self.numOfCH == 2:
self.photonDecayCh2, self.decayScale2 = delayTime2bin(
np.array(self.dTimeArr), np.array(self.subChanArr),
self.ch_present[1], self.winInt)
#Time series of photon counts. For visualisation.
self.timeSeries1, self.timeSeriesScale1 = delayTime2bin(
np.array(self.trueTimeArr) / 1000000, np.array(self.subChanArr),
self.ch_present[0], self.photonCountBin)
unit = self.timeSeriesScale1[-1] / self.timeSeriesScale1.__len__()
#Converts to counts per
self.kcount_CH1 = np.average(self.timeSeries1)
raw_count = np.average(
self.timeSeries1
) #This is the unnormalised intensity count for int_time duration (the first moment)
var_count = np.var(self.timeSeries1)
self.brightnessNandBCH0 = (((var_count - raw_count) / (raw_count)) /
(float(unit)))
if (var_count - raw_count) == 0:
self.numberNandBCH0 = 0
else:
self.numberNandBCH0 = (raw_count**2 / (var_count - raw_count))
if self.numOfCH == 2:
self.timeSeries2, self.timeSeriesScale2 = delayTime2bin(
np.array(self.trueTimeArr) / 1000000,
np.array(self.subChanArr), self.ch_present[1],
self.photonCountBin)
unit = self.timeSeriesScale2[-1] / self.timeSeriesScale2.__len__()
self.kcount_CH2 = np.average(self.timeSeries2)
raw_count = np.average(
self.timeSeries2
) #This is the unnormalised intensity count for int_time duration (the first moment)
var_count = np.var(self.timeSeries2)
self.brightnessNandBCH1 = (((var_count - raw_count) /
(raw_count)) / (float(unit)))
if (var_count - raw_count) == 0:
self.numberNandBCH1 = 0
else:
self.numberNandBCH1 = (raw_count**2 / (var_count - raw_count))
self.CV = calc_coincidence_value(self)
#Calculates the Auto and Cross-correlation functions.
self.crossAndAuto(np.array(self.trueTimeArr),
np.array(self.subChanArr))
if self.fit_obj != None:
#If fit object provided then creates fit objects.
if self.objId1 == None:
corrObj = corrObject(self.filepath, self.fit_obj)
self.objId1 = corrObj.objId
self.objId1.parent_name = 'point FCS'
self.objId1.parent_uqid = 'point FCS'
self.fit_obj.objIdArr.append(corrObj.objId)
self.objId1.param = copy.deepcopy(self.fit_obj.def_param)
self.objId1.name = self.name + '_CH0_Auto_Corr'
self.objId1.ch_type = 0 #channel 0 Auto
self.objId1.siblings = None
self.objId1.prepare_for_fit()
self.objId1.kcount = self.kcount_CH1
self.objId1.autoNorm = np.array(self.autoNorm[:, 0, 0]).reshape(-1)
self.objId1.autotime = np.array(self.autotime).reshape(-1)
self.objId1.param = copy.deepcopy(self.fit_obj.def_param)
self.objId1.max = np.max(self.objId1.autoNorm)
self.objId1.min = np.min(self.objId1.autoNorm)
if self.numOfCH == 2:
self.objId1.CV = self.CV
if self.objId3 == None:
corrObj = corrObject(self.filepath, self.fit_obj)
self.objId3 = corrObj.objId
self.objId3.parent_name = 'point FCS'
self.objId3.parent_uqid = 'point FCS'
self.objId3.param = copy.deepcopy(self.fit_obj.def_param)
self.fit_obj.objIdArr.append(corrObj.objId)
self.objId3.name = self.name + '_CH1_Auto_Corr'
self.objId3.ch_type = 1 #channel 1 Auto
self.objId3.siblings = None
self.objId3.prepare_for_fit()
self.objId3.kcount = self.kcount_CH2
self.objId3.autoNorm = np.array(self.autoNorm[:, 1,
1]).reshape(-1)
self.objId3.autotime = np.array(self.autotime).reshape(-1)
self.objId3.param = copy.deepcopy(self.fit_obj.def_param)
self.objId3.max = np.max(self.objId3.autoNorm)
self.objId3.min = np.min(self.objId3.autoNorm)
self.objId3.CV = self.CV
if self.objId2 == None:
corrObj = corrObject(self.filepath, self.fit_obj)
self.objId2 = corrObj.objId
self.objId2.parent_name = 'point FCS'
self.objId2.parent_uqid = 'point FCS'
self.objId2.param = copy.deepcopy(self.fit_obj.def_param)
self.fit_obj.objIdArr.append(corrObj.objId)
self.objId2.name = self.name + '_CH01_Cross_Corr'
self.objId2.ch_type = 2 #01cross
self.objId2.siblings = None
self.objId2.prepare_for_fit()
self.objId2.autoNorm = np.array(self.autoNorm[:, 0,
1]).reshape(-1)
self.objId2.autotime = np.array(self.autotime).reshape(-1)
self.objId2.param = copy.deepcopy(self.fit_obj.def_param)
self.objId2.max = np.max(self.objId2.autoNorm)
self.objId2.min = np.min(self.objId2.autoNorm)
self.objId2.CV = self.CV
if self.objId4 == None:
corrObj = corrObject(self.filepath, self.fit_obj)
self.objId4 = corrObj.objId
self.objId4.parent_name = 'point FCS'
self.objId4.parent_uqid = 'point FCS'
self.objId4.param = copy.deepcopy(self.fit_obj.def_param)
self.fit_obj.objIdArr.append(corrObj.objId)
self.objId4.name = self.name + '_CH10_Cross_Corr'
self.objId4.ch_type = 3 #10cross
self.objId4.siblings = None
self.objId4.prepare_for_fit()
self.objId4.autoNorm = np.array(self.autoNorm[:, 1,
0]).reshape(-1)
self.objId4.autotime = np.array(self.autotime).reshape(-1)
self.objId4.param = copy.deepcopy(self.fit_obj.def_param)
self.objId4.max = np.max(self.objId4.autoNorm)
self.objId4.min = np.min(self.objId4.autoNorm)
self.objId4.CV = self.CV
self.fit_obj.fill_series_list()
self.dTimeMin = 0
self.dTimeMax = np.max(self.dTimeArr)
self.subDTimeMin = self.dTimeMin
self.subDTimeMax = self.dTimeMax
self.exit = False
#del self.subChanArr
#del self.trueTimeArr
del self.dTimeArr
def produce_plots():
# Generate Arrays of Random numbers
test_arrays = [
np.random.random((2**5, 2**5)),
np.random.random((2**6, 2**6)),
np.random.random(
(2**7, 2**7)), # DFT Mean around 5.5s, var around 0.0119s
np.random.random(
(2**8, 2**8)), # DFT Mean around 42.838s, var around 1.19s
np.random.random((
2**9,
2**9)), # DFT Mean around 372.367s, var around 5.299s (Super long)
np.random.random((2**10, 2**10)) # (Going to take way too long to run)
]
# Store results
dimensions_array = []
dft_mean_array = []
dft_variance_array = []
fft_mean_array = []
fft_variance_array = []
for array in test_arrays:
# Store problem size and append to designated array
dimension = array.shape[0]
dimensions_array.append(dimension)
dft_results = []
fft_results = []
for i in range(10):
# Naive DFT Method
start_time = time.time()
fouriertransform.dft_2d(array)
end_time = time.time()
dft_results.append(end_time - start_time)
# FFT Method
start_time = time.time()
fouriertransform.fft_2d(array)
end_time = time.time()
fft_results.append(end_time - start_time)
# Store dft mean and variance
dft_mean = np.mean(dft_results)
dft_mean_array.append(dft_mean)
dft_variance = np.var(dft_results)
dft_variance_array.append(dft_variance)
# Store fft mean and variance
fft_mean = np.mean(fft_results)
fft_mean_array.append(fft_mean)
fft_variance = np.var(fft_results)
fft_variance_array.append(fft_variance)
# Print mean and variance
print('Array Dimensions: {} by {}'.format(dimension, dimension))
print("----------------------------------------")
print("DFT Mean: ", dft_mean)
print("FFT Mean: ", np.mean(fft_results))
print("DFT Variance: ", dft_variance)
print("FFT Variance: ", np.var(fft_results))
print("----------------------------------------\n")
# Plot Results
# Error is standard deviation * 2
dft_errors = [math.sqrt(i) * 2 for i in dft_variance_array]
fft_errors = [math.sqrt(i) * 2 for i in fft_variance_array]
plt.errorbar(dimensions_array,
dft_mean_array,
yerr=dft_errors,
ecolor="red",
label='DFT')
plt.errorbar(dimensions_array,
fft_mean_array,
color='green',
yerr=fft_errors,
ecolor="red",
label='FFT')
plt.title('Mean Time vs Problem Size')
plt.xlabel('Problem Size', fontsize=14)
plt.ylabel('Runtime (s)', fontsize=14)
plt.legend(loc='upper left')
plt.grid(True)
plt.show()
def run_CV(self):
cvIter = 0
totalInstanceNum = len(self.m_targetLabel)
print("totalInstanceNum\t", totalInstanceNum)
indexList = [i for i in range(totalInstanceNum)]
totalTransferNumList = []
np.random.seed(3)
np.random.shuffle(indexList)
foldNum = 10
foldInstanceNum = int(totalInstanceNum * 1.0 / foldNum)
foldInstanceList = []
for foldIndex in range(foldNum - 1):
foldIndexInstanceList = indexList[foldIndex *
foldInstanceNum:(foldIndex + 1) *
foldInstanceNum]
foldInstanceList.append(foldIndexInstanceList)
foldIndexInstanceList = indexList[foldInstanceNum * (foldNum - 1):]
foldInstanceList.append(foldIndexInstanceList)
totalAccList = [[] for i in range(10)]
humanAccList = [[] for i in range(10)]
correctTransferRatioList = []
totalTransferNumList = []
correctTransferLabelNumList = []
correctUntransferRatioList = []
totalAuditorPrecisionList = []
totalAuditorRecallList = []
totalAuditorAccList = []
for foldIndex in range(foldNum):
if self.m_multipleClass:
self.m_clf = LR(multi_class="multinomial",
solver='lbfgs',
random_state=3)
else:
self.m_clf = LR(random_state=3)
self.m_auditor0 = LR(random_state=3)
self.m_auditor1 = LR(random_state=3)
train = []
for preFoldIndex in range(foldIndex):
train.extend(foldInstanceList[preFoldIndex])
test = foldInstanceList[foldIndex]
for postFoldIndex in range(foldIndex + 1, foldNum):
train.extend(foldInstanceList[postFoldIndex])
trainNum = int(totalInstanceNum * 0.9)
targetNameFeatureTrain = self.m_targetNameFeature[train]
targetLabelTrain = self.m_targetLabel[train]
# targetDataFeatureTrain = self.m_targetDataFeature[train]
targetNameFeatureTest = self.m_targetNameFeature[test]
targetLabelTest = self.m_targetLabel[test]
# transferLabelTest = self.m_transferLabel[test]
transferLabelTest = []
initExList = []
initExList = self.pretrainSelectInit(train, foldIndex)
# random.seed(101)
# initExList = random.sample(train, 3)
targetNameFeatureInit = self.m_targetNameFeature[initExList]
targetLabelInit = self.m_targetLabel[initExList]
print("initExList\t", initExList, targetLabelInit)
queryIter = 0
labeledExList = []
unlabeledExList = []
###labeled index
labeledExList.extend(initExList)
unlabeledExList = list(set(train) - set(labeledExList))
activeLabelNum = 3.0
transferLabelNum = 0.0
transferFeatureList = []
transferFlagList0 = []
transferFlagList1 = []
featureDim = len(targetNameFeatureTrain[0])
self.init_confidence_bound(featureDim, labeledExList,
unlabeledExList)
targetNameFeatureIter = targetNameFeatureInit
targetLabelIter = targetLabelInit
correctTransferLabelNum = 0.0
wrongTransferLabelNum = 0.0
correctUntransferLabelNum = 0.0
wrongUntransferLabelNum = 0.0
# auditorPrecisionList = []
# auditorRecallList = []
auditorAccList = []
while activeLabelNum < rounds:
# targetNameFeatureIter = self.m_targetNameFeature[labeledExList]
# targetLabelIter = self.m_targetLabel[labeledExList]
self.m_clf.fit(targetNameFeatureIter, targetLabelIter)
exId = self.select_example(unlabeledExList)
# self.update_select_confidence_bound(exId)
# print(idx)
activeLabelFlag = False
transferLabelFlag, weakOracleIndex, transferLabel = self.get_transfer_flag(
transferFeatureList, transferFlagList0, transferFlagList1,
exId, activeLabelNum)
exLabel = -1
if transferLabelFlag:
self.m_weakLabeledIDList.append(exId)
transferLabelNum += 1.0
activeLabelFlag = False
exLabel = transferLabel
targetNameFeatureIter = np.vstack(
(targetNameFeatureIter,
self.m_targetNameFeature[exId]))
targetLabelIter = np.hstack((targetLabelIter, exLabel))
# targetNameFeatureIter.append(self.m_targetNameFeature[exId])
# targetLabelIter.append(exLabel)
if exLabel == self.m_targetLabel[exId]:
correctTransferLabelNum += 1.0
print("queryIter\t", queryIter)
else:
wrongTransferLabelNum += 1.0
print("query iteration", queryIter,
"error transfer label\t", exLabel, "true label",
self.m_targetLabel[exId])
else:
self.m_strongLabeledIDList.append(exId)
self.update_judge_confidence_bound(exId)
activeLabelNum += 1.0
activeLabelFlag = True
exLabel = self.m_targetLabel[exId]
targetNameFeatureIter = np.vstack(
(targetNameFeatureIter,
self.m_targetNameFeature[exId]))
targetLabelIter = np.hstack((targetLabelIter, exLabel))
# targetNameFeatureIter.append(self.m_targetNameFeature[exId])
# targetLabelIter.append(exLabel)
weakLabel0 = self.m_transferLabel0[exId]
weakLabel1 = self.m_transferLabel1[exId]
transferFeatureList.append(self.m_targetNameFeature[exId])
if weakLabel0 == exLabel:
correctUntransferLabelNum += 1.0
transferFlagList0.append(1.0)
else:
wrongUntransferLabelNum += 1.0
transferFlagList0.append(0.0)
if weakLabel1 == exLabel:
correctUntransferLabelNum += 1.0
transferFlagList1.append(1.0)
else:
wrongUntransferLabelNum += 1.0
transferFlagList1.append(0.0)
auditorAcc = self.getAuditorMetric(transferFeatureList,
transferFlagList0,
transferFlagList1,
targetNameFeatureTest,
transferLabelTest,
targetLabelTest)
print("auditorAcc", auditorAcc)
auditorAccList.append(auditorAcc)
labeledExList.append(exId)
unlabeledExList.remove(exId)
acc = self.get_pred_acc(targetNameFeatureTest, targetLabelTest,
targetNameFeatureIter, targetLabelIter)
totalAccList[cvIter].append(acc)
if activeLabelFlag:
humanAccList[cvIter].append(acc)
queryIter += 1
totalAuditorAccList.append(auditorAccList)
transferLabelNum = len(self.m_weakLabeledIDList)
totalTransferNumList.append(transferLabelNum)
correctTransferLabelNumList.append(correctTransferLabelNum)
cvIter += 1
print("transfer num\t", np.mean(totalTransferNumList),
np.sqrt(np.var(totalTransferNumList)))
print("correct transfer num\t", np.mean(correctTransferLabelNumList),
np.sqrt(np.var(correctTransferLabelNumList)))
AuditorAccFile = modelVersion + "_auditor_acc.txt"
writeFile(totalAuditorAccList, AuditorAccFile)
totalACCFile = modelVersion + "_acc.txt"
writeFile(totalAccList, totalACCFile)
humanACCFile = modelVersion + "_human_acc.txt"
writeFile(humanAccList, humanACCFile)
def find_best_network(self, T_val=100):
self._open_hp_files()
best_network = None
best_ll = -float('inf')
best_tau = 0
best_dropout = 0
best_HIDDEN_UNITS = []
for dropout_rate in self._DROPOUT_RATES:
for tau in self._TAU_VALUES:
for n_hidden in self._HIDDEN_UNITS_FILE:
print ('Grid search step: Tau: ' + str(tau) + ' Dropout rate: ' + str(dropout_rate)+ ' Hidden units : ' + str(n_hidden))
network = self.mcd_model.model_runner(
self.X_train, self.y_train,
dropout_prob=dropout_rate,
n_epochs=self.n_epochs,
tau=tau,
batch_size=self.batch_size,
lengthscale=1e-2,
n_hidden=n_hidden
)
print('Starting prediction using validation data..')
probs_mc_dropout = []
self.model = network
T = T_val
for t_i in range(T):
print('T: ', t_i)
probs_mc_dropout += [self.model.predict(self.X_val, batch_size=self.batch_size, verbose=1)]
predictive_mean = np.mean(probs_mc_dropout, axis=0)
predictive_variance = np.var(probs_mc_dropout, axis=0)
# obtained the test ll from the validation sets
ll = self.log_likelihood(self.y_val, predictive_mean, tau, T)
if (ll > best_ll):
best_ll = ll
best_network = network
best_tau = tau
best_dropout = dropout_rate
best_HIDDEN_UNITS = n_hidden
print ('Best log_likelihood changed to: ' + str(best_ll))
print ('Best tau changed to: ' + str(best_tau))
print ('Best dropout rate changed to: ' + str(best_dropout))
self.best_tau_val = best_tau
self.best_dropout_val = best_dropout
self.best_HIDDEN_UNITS_lay = best_HIDDEN_UNITS
self.best_MSD_model = best_network
best_val = {
'best_tau': self.best_tau_val,
'best_dropout': self.best_dropout_val,
'best_HIDDEN_UNITS': self.best_HIDDEN_UNITS_lay
}
with open(self.hp_output_PATH, 'w') as fp:
json.dump(best_val, fp)
self.best_MSD_model.save(self.MCDmodel_output_PATH)
def plot_results(results,
W,
poss,
shape,
epochs,
explore_prop,
performance_check,
sample_amount=3):
# Check if the samples taken from the results is not more than the results
sample_amount_check = True
while sample_amount_check:
if sample_amount * 2 > len(results):
sample_amount -= 1
print("sample_amount reduced by 1")
else:
sample_amount_check = False
top_results = []
mean_var_top_results = []
top_poss = []
bottom_results = []
mean_var_bottom_results = []
bottom_poss = []
title = f"Value at highest {sample_amount} states"
# Remove end state (2,2)
del results[2 * shape[1] + 2]
del poss[2 * shape[1] + 2]
for i in range(sample_amount):
index_top = np.argmax(results, axis=0)[epochs - 1]
top_results.append(results[index_top])
mean_var_top_results.append(
(np.mean(results[index_top][:int(explore_prop * epochs)]),
np.var(results[index_top][:int(explore_prop * epochs)])))
top_poss.append(poss[index_top])
del results[index_top]
del poss[index_top]
is_wall = True
while is_wall:
index_bottom = np.argmin(results, axis=0)[epochs - 1]
if poss[index_bottom] in W:
del results[index_bottom]
del poss[index_bottom]
else:
is_wall = False
bottom_results.append(results[index_bottom])
mean_var_bottom_results.append(
(np.mean(results[index_bottom][:int(explore_prop * epochs)]),
np.var(results[index_bottom][:int(explore_prop * epochs)])))
bottom_poss.append(poss[index_bottom])
del results[index_bottom]
del poss[index_bottom]
print_var = False
for i in range(len(top_poss)):
plt.plot(top_results[i],
label=(f"{top_poss[i]}" + f" Top No. {i+1}" +
(" #Start" if top_poss[i] == (5, 0) else
" #End" if top_poss[i] == (2, 2) else "") +
(f" {mean_var_top_results[i]}" if print_var else "")))
plt.xlabel("Epochs")
plt.ylabel("Value")
xmin, xmax, ymin, ymax = plt.axis()
# plt.vlines(epochs*explore_prop, ymin, ymax, label="explore to exploit", linestyle="dotted")
plt.legend(loc="best", framealpha=1)
plt.title(f"Value at the highest {sample_amount} states")
plt.draw()
plt.waitforbuttonpress()
plt.clf()
plt.plot(performance_check)
plt.title("Learning curve")
plt.xlabel("Epochs")
plt.ylabel("Error in shortest paths")
plt.draw()
plt.waitforbuttonpress()
plt.clf()
# for i in range(len(bottom_poss)):
# plt.plot(bottom_results[i], label=(f"{bottom_poss[i]}" + f" Bottom No. {i+1}" + (" #Start" if bottom_poss[i]==(5,0) else " #End" if bottom_poss[i] == (2,2) else "") + (f" {mean_var_bottom_results[i]}" if print_var else "")))
# plt.xlabel("Epochs")
# plt.ylabel("Value")
# xmin, xmax, ymin, ymax = plt.axis()
# plt.vlines(epochs*explore_prop, ymin, ymax, label="explore to exploit", linestyle="dotted")
# plt.legend(loc="best", framealpha=1)
# plt.title(f"Value at the lowest {sample_amount} states")
# plt.draw()
# plt.waitforbuttonpress()
# plt.clf()
plt.close()
def batchnorm_forward(x, gamma, beta, bn_param):
"""
Forward pass for batch normalization.
During training the sample mean and (uncorrected) sample variance are
computed from minibatch statistics and used to normalize the incoming data.
During training we also keep an exponentially decaying running mean of the
mean and variance of each feature, and these averages are used to normalize
data at test-time.
At each timestep we update the running averages for mean and variance using
an exponential decay based on the momentum parameter:
running_mean = momentum * running_mean + (1 - momentum) * sample_mean
running_var = momentum * running_var + (1 - momentum) * sample_var
Note that the batch normalization paper suggests a different test-time
behavior: they compute sample mean and variance for each feature using a
large number of training images rather than using a running average. For
this implementation we have chosen to use running averages instead since
they do not require an additional estimation step; the torch7
implementation of batch normalization also uses running averages.
Input:
- x: Data of shape (N, D)
- gamma: Scale parameter of shape (D,)
- beta: Shift paremeter of shape (D,)
- bn_param: Dictionary with the following keys:
- mode: 'train' or 'test'; required
- eps: Constant for numeric stability
- momentum: Constant for running mean / variance.
- running_mean: Array of shape (D,) giving running mean of features
- running_var Array of shape (D,) giving running variance of features
Returns a tuple of:
- out: of shape (N, D)
- cache: A tuple of values needed in the backward pass
"""
mode = bn_param['mode']
eps = bn_param.get('eps', 1e-5)
momentum = bn_param.get('momentum', 0.9)
N, D = x.shape
running_mean = bn_param.get('running_mean', np.zeros(D, dtype=x.dtype))
running_var = bn_param.get('running_var', np.zeros(D, dtype=x.dtype))
out, cache = None, None
if mode == 'train':
#######################################################################
# TODO: Implement the training-time forward pass for batch norm. #
# Use minibatch statistics to compute the mean and variance, use #
# these statistics to normalize the incoming data, and scale and #
# shift the normalized data using gamma and beta. #
# #
# You should store the output in the variable out. Any intermediates #
# that you need for the backward pass should be stored in the cache #
# variable. #
# #
# You should also use your computed sample mean and variance together #
# with the momentum variable to update the running mean and running #
# variance, storing your result in the running_mean and running_var #
# variables. #
#######################################################################
sample_mean = np.mean(x, axis=0)
sample_var = np.var(x, axis=0)
x_norm = (x - sample_mean) / np.sqrt(sample_var + eps)
out = x_norm * gamma + beta
running_mean = momentum * running_mean + (1 - momentum) * sample_mean
running_var = momentum * running_var + (1 - momentum) * sample_var
cache = (x_norm, gamma, x - sample_mean, 1 / np.sqrt(sample_var + eps))
#######################################################################
# END OF YOUR CODE #
#######################################################################
elif mode == 'test':
#######################################################################
# TODO: Implement the test-time forward pass for batch normalization. #
# Use the running mean and variance to normalize the incoming data, #
# then scale and shift the normalized data using gamma and beta. #
# Store the result in the out variable. #
#######################################################################
x_norm = (x - running_mean) / np.sqrt(running_var + eps)
out = x_norm * gamma + beta
#######################################################################
# END OF YOUR CODE #
#######################################################################
else:
raise ValueError('Invalid forward batchnorm mode "%s"' % mode)
# Store the updated running means back into bn_param
bn_param['running_mean'] = running_mean
bn_param['running_var'] = running_var
return out, cache
def outputTemp(Mean, Variance):
print(
time.strftime("%H:%M:%S", time.localtime()) +
" {:>5.2f} {:>4.3f}".format(Mean, Variance))
if __name__ == '__main__':
outputTimeStep = 1
screenOutput = False
filename = time.strftime("%m-%d-at-%H:%M-year%YRecordLog.txt",
time.localtime())
with open(filename, 'w') as f:
if screenOutput:
outputBanner()
f.write("{} {} {}\n".format('Time', 'ObjectiveTemp', 'Variance'))
while True:
startTime = time.time()
tempList = []
while time.time() - startTime < outputTimeStep:
tempList.append(chanRef.voltage * 10)
time.sleep(outputTimeStep // 50)
tempArray = np.array(tempList)
Mean, Var = np.mean(tempArray), np.var(tempArray)
f.write(
time.strftime("%H:%M:%S", time.localtime()) +
" {:>5.2f} {:>4.3f}\n".format(Mean, Var))
if screenOutput:
outputTemp(Mean, Var)
def test_hermitian(self, device, tol, skip_if):
"""Test that a tensor product involving qml.Hermitian works correctly"""
n_wires = 3
dev = device(n_wires)
skip_if(dev, {"supports_tensor_observables": False})
theta = 0.432
phi = 0.123
varphi = -0.543
A_ = 0.1 * np.array([
[-6, 2 + 1j, -3, -5 + 2j],
[2 - 1j, 0, 2 - 1j, -5 + 4j],
[-3, 2 + 1j, 0, -4 + 3j],
[-5 - 2j, -5 - 4j, -4 - 3j, -6],
])
@qml.qnode(dev)
def circuit():
qml.RX(theta, wires=[0])
qml.RX(phi, wires=[1])
qml.RX(varphi, wires=[2])
qml.CNOT(wires=[0, 1])
qml.CNOT(wires=[1, 2])
return qml.sample(
qml.PauliZ(wires=[0]) @ qml.Hermitian(A_, wires=[1, 2]))
res = circuit()
# res should only contain the eigenvalues of
# the hermitian matrix tensor product Z
Z = np.diag([1, -1])
eigvals = np.linalg.eigvalsh(np.kron(Z, A_))
assert np.allclose(sorted(np.unique(res)),
sorted(eigvals),
atol=tol(False))
mean = np.mean(res)
expected = (0.1 * 0.5 *
(-6 * np.cos(theta) *
(np.cos(varphi) + 1) - 2 * np.sin(varphi) *
(np.cos(theta) + np.sin(phi) - 2 * np.cos(phi)) +
3 * np.cos(varphi) * np.sin(phi) + np.sin(phi)))
assert np.allclose(mean, expected, atol=tol(False))
var = np.var(res)
expected = (
0.01 *
(1057 - np.cos(2 * phi) + 12 *
(27 + np.cos(2 * phi)) * np.cos(varphi) -
2 * np.cos(2 * varphi) * np.sin(phi) *
(16 * np.cos(phi) + 21 * np.sin(phi)) + 16 * np.sin(2 * phi) - 8 *
(-17 + np.cos(2 * phi) + 2 * np.sin(2 * phi)) * np.sin(varphi) -
8 * np.cos(2 * theta) *
(3 + 3 * np.cos(varphi) + np.sin(varphi))**2 - 24 * np.cos(phi) *
(np.cos(phi) + 2 * np.sin(phi)) * np.sin(2 * varphi) -
8 * np.cos(theta) *
(4 * np.cos(phi) *
(4 + 8 * np.cos(varphi) + np.cos(2 * varphi) -
(1 + 6 * np.cos(varphi)) * np.sin(varphi)) + np.sin(phi) *
(15 + 8 * np.cos(varphi) - 11 * np.cos(2 * varphi) +
42 * np.sin(varphi) + 3 * np.sin(2 * varphi)))) / 16)
assert np.allclose(var, expected, atol=tol(False))
def makeViolinPlots(dataForViolinO, theAx, ytitle):
dataForViolin = dict()
for ke in dataForViolinO.keys():
# if ke != 'GSE47652':
# continue
dataForViolin[ke] = dataForViolinO[ke]
ax = theAx
titles = dataForViolin.keys()
allData = []
numFns = len(titles)
for ke in titles:
dataRAND = dataForViolin[ke][1]
if np.var(dataRAND) < 0.000001:
dataRAND[0] = dataRAND[1] + 0.0001
dataRSS = dataForViolin[ke][2]
if np.var(dataRSS) < 0.000001:
dataRSS[0] = dataRSS[1] + 0.0001
allData.append(dataRAND)
allData.append(dataRSS)
positions = [[0.7 * i, 0.7 * i] for i in range(1, numFns + 1)]
positions = [item for sublist in positions for item in sublist]
ax.plot([positions[0] - 1.05, positions[-1] + 0.35], [1, 1],
'r:',
alpha=0.7,
zorder=-1)
violin_parts = ax.violinplot(allData,
showmeans=True,
positions=positions,
showextrema=False)
# print(str(violin_parts['cmeans'].get_segments()))
count = 0
Osegs = violin_parts['cmeans'].get_segments()
segs = []
for b in violin_parts['bodies']:
# b.set_alpha(0.75)
thisSeg = Osegs[count]
midPointX = thisSeg[0][0] + (thisSeg[1][0] - thisSeg[0][0]) / 2.0
if count % 2 == 0:
m = np.mean(b.get_paths()[0].vertices[:, 0])
b.get_paths()[0].vertices[:, 0] = np.clip(
b.get_paths()[0].vertices[:, 0], -np.inf, m)
b.set_color('b')
b.set_facecolor('blue')
newSeg = np.array([[thisSeg[0][0], thisSeg[0][1]],
[midPointX + 0.01, thisSeg[1][1]]])
else:
m = np.mean(b.get_paths()[0].vertices[:, 0])
b.get_paths()[0].vertices[:, 0] = np.clip(
b.get_paths()[0].vertices[:, 0], m, np.inf)
b.set_color('g')
b.set_facecolor('green')
newSeg = np.array([[midPointX - 0.01, thisSeg[0][1]],
[thisSeg[1][0], thisSeg[1][1]]])
count += 1
segs.append(newSeg)
b.set_alpha(0.99)
violin_parts['cmeans'].set_color('black')
# print("seg:")
violin_parts['cmeans'].set_segments(segs)
# print(str(violin_parts['cmeans'].get_segments()))
# print("__\n")
ax.set_xticks([0.7 * i for i in range(1, numFns + 1)])
ax.set_xticklabels(titles, rotation='vertical')
ax.set_ylabel(ytitle)
ylim = list(ax.get_ylim())
if ylim[0] < 1 and ylim[1] <= 1:
ylim[1] = 1.1
if ylim[0] >= 1 and ylim[1] > 1:
ylim[0] = 0.9
ax.set_ylim(ylim)
ax.set_yticks(ax.get_yticks()[1:-1])
# Custom legend
import matplotlib.patches as mpatches
pB = mpatches.Patch(color='blue', linewidth=0)
pG = mpatches.Patch(color='green', linewidth=0)
MpsdWelch = periodogramaWelch(x,K,O) #Este es el implementado por mi
psdWelch = np.zeros(shape=(201,R))
for i in range(R):
f,psdWelch[:,i] = signal.welch(x[:,i], fs, 'bartlett',nperseg=400, noverlap=200) # ver: scipy.signal.welch
plt.figure(1)
plt.plot(f,20*np.log10(psdWelch))
Mf = np.arange(len(MpsdWelch))*(fs/(len(MpsdWelch)*2))
plt.figure(2)
plt.plot(Mf,20*np.log10(MpsdWelch))
# Obtengo la frecuencia de la senoidal utilizando el estimador
estF0 = f[np.argmax(psdWelch,axis=0)]
valEspF0 = np.mean(estF0)
varEstF0 = np.var(estF0)
print(estF0)
print(valEspF0)
print(varEstF0)
'''
f = np.arange(len(psdWelch))*(fs/(len(psdWelch)*2))
# Obtengo la frecuencia de la senoidal utilizando el estimador
estF0 = f[np.argmax(psdWelch,axis=0)]
valEspF0 = np.mean(estF0)
varEstF0 = np.var(estF0)
except Exception: # protect against API changes
pass
if origVariance:
fig.suptitle("Diffim residuals: Normalized by sqrt(input variance)", fontsize=titleFs)
else:
fig.suptitle("Diffim residuals: Normalized by sqrt(diffim variance)", fontsize=titleFs)
sp1 = pylab.subplot(221)
sp2 = pylab.subplot(222, sharex=sp1, sharey=sp1)
sp3 = pylab.subplot(223, sharex=sp1, sharey=sp1)
sp4 = pylab.subplot(224, sharex=sp1, sharey=sp1)
xs = np.arange(-5, 5.05, 0.1)
ys = 1. / np.sqrt(2 * np.pi) * np.exp( -0.5 * xs**2 )
sp1.hist(candidateResids, bins=xs, normed=True, alpha=0.5, label="N(%.2f, %.2f)"
% (np.mean(candidateResids), np.var(candidateResids)))
sp1.plot(xs, ys, "r-", lw=2, label="N(0,1)")
sp1.set_title("Candidates: basis fit", fontsize=titleFs-2)
sp1.legend(loc=1, fancybox=True, shadow=True, prop = FontProperties(size=titleFs-6))
sp2.hist(spatialResids, bins=xs, normed=True, alpha=0.5, label="N(%.2f, %.2f)"
% (np.mean(spatialResids), np.var(spatialResids)))
sp2.plot(xs, ys, "r-", lw=2, label="N(0,1)")
sp2.set_title("Candidates: spatial fit", fontsize=titleFs-2)
sp2.legend(loc=1, fancybox=True, shadow=True, prop = FontProperties(size=titleFs-6))
sp3.hist(nonfitResids, bins=xs, normed=True, alpha=0.5, label="N(%.2f, %.2f)"
% (np.mean(nonfitResids), np.var(nonfitResids)))
sp3.plot(xs, ys, "r-", lw=2, label="N(0,1)")
sp3.set_title("Control sample: spatial fit", fontsize=titleFs-2)
sp3.legend(loc=1, fancybox=True, shadow=True, prop = FontProperties(size=titleFs-6))
data = mat_file.get('arrhythmia')
data = data[~np.all(data == 0, axis=1)] # deleting eventual zero columns
class_id = data[:, -1]
class_id[np.where(class_id > 1)] = 2
class_id = class_id - 1
data = data[:, :-1]
(N, F) = np.shape(data)
mean = np.mean(data)
std = np.std(data)
x_norm = (data - mean) / std
mean = np.mean(x_norm, 0)
var = np.var(x_norm, 0)
n_healthy = sum(class_id == 0)
n_ill = sum(class_id == 1)
# initializing the neural network graph
tf.set_random_seed(1234)
learning_rate = 1e-4
n_hidden_nodes_1 = F
n_hidden_nodes_2 = 128
x = tf.placeholder(tf.float64, [N, F])
t = tf.placeholder(tf.float64, [N, 1])
# first layer
w1 = tf.Variable(
def func(N):
sum = 0
for i in range(N):
x, y = np.random.rand(2)
sum += np.exp(5 * abs(x - 5) + 5 * abs(y - 5))
return sum / N
s = []
for i in range(5):
print(func(100))
for i in range(50):
s.append(func(1000))
print(np.var(s))
def func1(N):
sum = 0
for i in range(N):
x, y = np.random.rand(2) * 2 - [1, 1]
sum += math.cos(math.pi + 5 * x + 5 * y)
return 2 * 2 * sum / N
b = []
for i in range(5):
print(func1(100))
for i in range(50):
b.append(func1(1000))
y_train = data_train[:,0]
y_test = data_test_val[:,0]
# normalize x and y
num_classes = len(np.unique(y_train))
'''
base = np.min(y_train) #Check if data is 0-based
if base != 0:
y_train -= base
y_test -= base
'''
if input_norm:
mean = np.mean(X_train,axis=0)
variance = np.var(X_train,axis=0)
X_train -= mean
#The 1e-9 avoids dividing by zero
X_train /= np.sqrt(variance)+1e-9
X_test -= mean
X_test /= np.sqrt(variance)+1e-9
#epochs = np.floor(batch_size*max_iterations / N)
#print('Train with approximately %d epochs' %(epochs))
# place for the input variables
x = tf.placeholder("float", shape=[None, D], name = 'Input_data')
y_ = tf.placeholder(tf.int64, shape=[None], name = 'Ground_truth')
keep_prob = tf.placeholder("float")
bn_train = tf.placeholder(tf.bool) #Boolean value to guide batchnorm
matrix_np = np.array(matrix)
print(matrix_np)
print("sum ", matrix_np.sum())
print(matrix_np[1, 1])
print("min in array", matrix_np.min())
print("min in row", matrix_np.min(axis=0))
print("min in column", matrix_np.min(axis=1))
print("max in array", matrix_np.max())
print("max in row", matrix_np.max(axis=0))
print("max in column ", matrix_np.max(axis=1))
print("sum row", matrix_np.sum(axis=0))
print("sum column", matrix_np.sum(axis=1))
print("mean column", matrix_np.mean(axis=1))
print("mean row", matrix_np.mean(axis=0))
print("var ", np.var(matrix_np))
print("std ", np.std(matrix_np))
print("median ", np.median(matrix_np))
################ sqrt, sin, log, abs #####################
print("add ", np.add(matrix_np, 5))
print("sqrt ", np.sqrt(matrix_np))
print("sin ", np.sin(matrix_np))
print("log ", np.log(matrix_np))
matrix_abs = [[-15, 35], [9, -36]]
print(np.abs(matrix_abs))
print(np.add(matrix_abs, matrix_np))
print(np.array_equal(matrix_np, matrix_np))
print(np.array_equal(matrix_np, matrix_abs))
print(np.ceil(random_numbers))
print(np.floor(random_numbers))
# 2. Compute frobenius norm of each timestep of the utterance
timestep_norms.extend(LA.norm(mat, axis=1))
utt_norms = sorted(utt_norms)
timestep_norms = sorted(timestep_norms)
fit_utt = stats.norm.pdf(utt_norms, np.mean(utt_norms), np.std(utt_norms))
fit_timestep = stats.norm.pdf(timestep_norms, np.mean(timestep_norms),
np.std(timestep_norms))
#print(skew(fit_utt), kurtosis(fit_utt))
plt.plot(utt_norms, fit_utt, '-r')
plt.hist(utt_norms, bins=50, normed=True, alpha=0.5)
m, v, s, k = round(float(np.mean(utt_norms)),
3), round(float(np.var(utt_norms)),
3), round(skew(fit_utt),
3), round(kurtosis(fit_utt), 3)
print(m, v, s, k)
plt.title("Mean {}, Var {}, Skew {}, Kurt {}".format(m, v, s, k))
plt.savefig(utt_norm_dist, dpi=300)
# Clear the figure
plt.clf()
plt.plot(timestep_norms, fit_timestep, '-r')
plt.hist(timestep_norms, bins=50, normed=True, alpha=0.5)
m, v, s, k = round(float(np.mean(timestep_norms)),
3), round(float(np.var(timestep_norms)),
3), round(skew(timestep_norms),
input_image = resized_image.transpose((2, 0, 1))
# Repeat image according to batch size for inference.
input_image = np.repeat(input_image[np.newaxis, :, :, :],
input_shape[0],
axis=0)
# Inference using Bayesian SegNet
start = time.time()
out = net.forward_all(data=input_image)
end = time.time()
print '%30s' % 'Executed Bayesian SegNet in ',\
str((end - start) * 1000), 'ms'
mean_confidence = np.mean(confidence_output, axis=0, dtype=np.float64)
var_confidence = np.var(confidence_output, axis=0, dtype=np.float64)
# Prepare segmented image results
classes = np.argmax(mean_confidence, axis=0)
segmentation_bgr = np.asarray(LABEL_COLOURS[classes]).astype(np.uint8)
segmented_image = overlay_segmentation_results(resized_image,
segmentation_bgr)
# Prepare confidence results
confidence = np.amax(mean_confidence, axis=0)
# Prepare uncertainty results
uncertainty = np.mean(var_confidence, axis=0, dtype=np.float64)
print(np.sqrt(np.mean(uncertainty)))
def _partial_fit(self, X, y, classes=None, _refit=False,
sample_weight=None):
"""Actual implementation of Gaussian NB fitting.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and
n_features is the number of features.
y : array-like, shape (n_samples,)
Target values.
classes : array-like, shape (n_classes,)
List of all the classes that can possibly appear in the y vector.
Must be provided at the first call to partial_fit, can be omitted
in subsequent calls.
_refit: bool
If true, act as though this were the first time we called
_partial_fit (ie, throw away any past fitting and start over).
sample_weight : array-like, shape (n_samples,), optional
Weights applied to individual samples (1. for unweighted).
Returns
-------
self : object
Returns self.
"""
X, y = check_X_y(X, y)
# If the ratio of data variance between dimensions is too small, it
# will cause numerical errors. To address this, we artificially
# boost the variance by epsilon, a small fraction of the standard
# deviation of the largest dimension.
epsilon = 1e-9 * np.var(X, axis=0).max()
if _refit:
self.classes_ = None
if _check_partial_fit_first_call(self, classes):
# This is the first call to partial_fit:
# initialize various cumulative counters
n_features = X.shape[1]
n_classes = len(self.classes_)
self.theta_ = np.zeros((n_classes, n_features))
self.sigma_ = np.zeros((n_classes, n_features))
self.class_prior_ = np.zeros(n_classes)
self.class_count_ = np.zeros(n_classes)
else:
if X.shape[1] != self.theta_.shape[1]:
msg = "Number of features %d does not match previous data %d."
raise ValueError(msg % (X.shape[1], self.theta_.shape[1]))
# Put epsilon back in each time
self.sigma_[:, :] -= epsilon
classes = self.classes_
unique_y = np.unique(y)
unique_y_in_classes = in1d(unique_y, classes)
if not np.all(unique_y_in_classes):
raise ValueError("The target label(s) %s in y do not exist in the "
"initial classes %s" %
(y[~unique_y_in_classes], classes))
for y_i in unique_y:
i = classes.searchsorted(y_i)
X_i = X[y == y_i, :]
if sample_weight is not None:
sw_i = sample_weight[y == y_i]
N_i = sw_i.sum()
else:
sw_i = None
N_i = X_i.shape[0]
new_theta, new_sigma = self._update_mean_variance(
self.class_count_[i], self.theta_[i, :], self.sigma_[i, :],
X_i, sw_i)
self.theta_[i, :] = new_theta
self.sigma_[i, :] = new_sigma
self.class_count_[i] += N_i
self.sigma_[:, :] += epsilon
self.class_prior_[:] = self.class_count_ / np.sum(self.class_count_)
return self
def versuch_auswerten(versuch_werte, versuch_name, header):
# Werte verarbeitbar machen
delta_l_values = pd.to_numeric(versuch_werte.delta_l_t).values
delta_r_values = pd.to_numeric(versuch_werte.delta_r_t).values
delta_m_values = pd.to_numeric(versuch_werte.delta_m_t).values
geschwindigkeit_l_values = pd.to_numeric(
versuch_werte.geschwindigkeit_l).values
geschwindigkeit_r_values = pd.to_numeric(
versuch_werte.geschwindigkeit_r).values
geschwindigkeit_m_values = pd.to_numeric(
versuch_werte.geschwindigkeit_m).values
tendenz_l_values = pd.to_numeric(versuch_werte.tendenz_l).values
tendenz_r_values = pd.to_numeric(versuch_werte.tendenz_r).values
tendenz_m_values = pd.to_numeric(versuch_werte.tendenz_m).values
blick_l_x_values = pd.to_numeric(versuch_werte.blick_l_x).values
blick_l_y_values = pd.to_numeric(versuch_werte.blick_l_y).values
blick_r_x_values = pd.to_numeric(versuch_werte.blick_r_x).values
blick_r_y_values = pd.to_numeric(versuch_werte.blick_r_y).values
sacc_m_values = pd.to_numeric(versuch_werte.sacc_m).values
sacc_l_values = pd.to_numeric(versuch_werte.sacc_l).values
sacc_r_values = pd.to_numeric(versuch_werte.sacc_r).values
# Mittelkwerte bestimmen
# Kein Exceptionhandling, da ein leeres Array dazu fuehrt, dass np.mean() nan zurueckgibt und keine Exception
if delta_l_values[np.nonzero(delta_l_values)].size == 0:
mean_delta_l = -1
else:
mean_delta_l = np.mean(delta_l_values[np.nonzero(delta_l_values)])
if delta_r_values[np.nonzero(delta_r_values)].size == 0:
mean_delta_r = -1
else:
mean_delta_r = np.mean(delta_r_values[np.nonzero(delta_r_values)])
if delta_m_values[np.nonzero(delta_m_values)].size == 0:
mean_delta_m = -1
else:
mean_delta_m = np.mean(delta_m_values[np.nonzero(delta_m_values)])
if geschwindigkeit_l_values[np.nonzero(
geschwindigkeit_l_values)].size == 0:
mean_geschwindigkeit_l = -1
else:
mean_geschwindigkeit_l = np.mean(
geschwindigkeit_l_values[np.nonzero(geschwindigkeit_l_values)])
if geschwindigkeit_r_values[np.nonzero(
geschwindigkeit_r_values)].size == 0:
mean_geschwindigkeit_r = -1
else:
mean_geschwindigkeit_r = np.mean(
geschwindigkeit_r_values[np.nonzero(geschwindigkeit_r_values)])
if geschwindigkeit_m_values[np.nonzero(
geschwindigkeit_m_values)].size == 0:
mean_geschwindigkeit_m = -1
else:
mean_geschwindigkeit_m = np.mean(
geschwindigkeit_m_values[np.nonzero(geschwindigkeit_m_values)])
header = np.append(header, [
versuch_name + '_mean_delta_l', versuch_name + '_mean_delta_r',
versuch_name + '_mean_delta_m', versuch_name +
'_mean_geschwindigkeit_l', versuch_name + '_mean_geschwindigkeit_r',
versuch_name + '_mean_geschwindigkeit_m'
])
# Maxima bestimmen
try:
max_delta_l = np.max(delta_l_values[np.nonzero(delta_l_values)])
except ValueError:
max_delta_l = -1
try:
max_delta_r = np.max(delta_r_values[np.nonzero(delta_r_values)])
except ValueError:
max_delta_r = -1
try:
max_delta_m = np.max(delta_m_values[np.nonzero(delta_m_values)])
except ValueError:
max_delta_m = -1
try:
max_geschwindigkeit_l = np.max(
geschwindigkeit_l_values[np.nonzero(geschwindigkeit_l_values)])
except ValueError:
max_geschwindigkeit_l = -1
try:
max_geschwindigkeit_r = np.max(
geschwindigkeit_r_values[np.nonzero(geschwindigkeit_r_values)])
except ValueError:
max_geschwindigkeit_r = -1
try:
max_geschwindigkeit_m = np.max(
geschwindigkeit_m_values[np.nonzero(geschwindigkeit_m_values)])
except ValueError:
max_geschwindigkeit_m = -1
header = np.append(header, [
versuch_name + '_max_delta_l', versuch_name + '_max_delta_r',
versuch_name + '_max_delta_m', versuch_name + '_max_geschwindigkeit_l',
versuch_name + '_max_geschwindigkeit_r',
versuch_name + '_max_geschwindigkeit_m'
])
# Minima bestimmen
#Exceptionhandling fuer die Versuchspersonen, bei denen nur ein Auge gemessen wurde
try:
min_delta_l = np.min(delta_l_values[np.nonzero(delta_l_values)])
except ValueError:
min_delta_l = -1
try:
min_delta_r = np.min(delta_r_values[np.nonzero(delta_r_values)])
except ValueError:
min_delta_r = -1
try:
min_delta_m = np.min(delta_m_values[np.nonzero(delta_m_values)])
except ValueError:
min_delta_m = -1
try:
min_geschwindigkeit_l = np.min(
geschwindigkeit_l_values[np.nonzero(geschwindigkeit_l_values)])
except ValueError:
min_geschwindigkeit_l = -1
try:
min_geschwindigkeit_r = np.min(
geschwindigkeit_r_values[np.nonzero(geschwindigkeit_r_values)])
except ValueError:
min_geschwindigkeit_r = -1
try:
min_geschwindigkeit_m = np.min(
geschwindigkeit_m_values[np.nonzero(geschwindigkeit_m_values)])
except ValueError:
min_geschwindigkeit_m = -1
header = np.append(header, [
versuch_name + '_min_delta_l', versuch_name + '_min_delta_r',
versuch_name + '_min_delta_m', versuch_name + '_min_geschwindigkeit_l',
versuch_name + '_min_geschwindigkeit_r',
versuch_name + '_min_geschwindigkeit_m'
])
# Standardabweichungen berechnen
if delta_l_values[np.nonzero(delta_l_values)].size == 0:
std_delta_l = -1
else:
std_delta_l = np.std(delta_l_values[np.nonzero(delta_l_values)])
if delta_r_values[np.nonzero(delta_r_values)].size == 0:
std_delta_r = -1
else:
std_delta_r = np.std(delta_r_values[np.nonzero(delta_r_values)])
if delta_m_values[np.nonzero(delta_m_values)].size == 0:
std_delta_m = -1
else:
std_delta_m = np.std(delta_m_values[np.nonzero(delta_m_values)])
if geschwindigkeit_l_values[np.nonzero(
geschwindigkeit_l_values)].size == 0:
std_geschwindigkeit_l = -1
else:
std_geschwindigkeit_l = np.std(
geschwindigkeit_l_values[np.nonzero(geschwindigkeit_l_values)])
if geschwindigkeit_r_values[np.nonzero(
geschwindigkeit_r_values)].size == 0:
std_geschwindigkeit_r = -1
else:
std_geschwindigkeit_r = np.std(
geschwindigkeit_r_values[np.nonzero(geschwindigkeit_r_values)])
if geschwindigkeit_m_values[np.nonzero(
geschwindigkeit_m_values)].size == 0:
std_geschwindigkeit_m = -1
else:
std_geschwindigkeit_m = np.std(
geschwindigkeit_m_values[np.nonzero(geschwindigkeit_m_values)])
header = np.append(header, [
versuch_name + '_standardabweichung_delta_l',
versuch_name + '_standardabweichung_delta_r',
versuch_name + '_standardabweichung_delta_m',
versuch_name + '_standardabweichung_geschwindigkeit_l',
versuch_name + '_standardabweichung_geschwindigkeit_r',
versuch_name + '_standardabweichung_geschwindigkeit_m'
])
# Varianzen berechnen
if delta_l_values[np.nonzero(delta_l_values)].size == 0:
var_delta_l = -1
else:
var_delta_l = np.var(delta_l_values[np.nonzero(delta_l_values)])
if delta_r_values[np.nonzero(delta_r_values)].size == 0:
var_delta_r = -1
else:
var_delta_r = np.var(delta_r_values[np.nonzero(delta_r_values)])
if delta_m_values[np.nonzero(delta_m_values)].size == 0:
var_delta_m = -1
else:
var_delta_m = np.var(delta_m_values[np.nonzero(delta_m_values)])
if geschwindigkeit_l_values[np.nonzero(
geschwindigkeit_l_values)].size == 0:
var_geschwindigkeit_l = -1
else:
var_geschwindigkeit_l = np.var(
geschwindigkeit_l_values[np.nonzero(geschwindigkeit_l_values)])
if geschwindigkeit_r_values[np.nonzero(
geschwindigkeit_r_values)].size == 0:
var_geschwindigkeit_r = -1
else:
var_geschwindigkeit_r = np.var(
geschwindigkeit_r_values[np.nonzero(geschwindigkeit_r_values)])
if geschwindigkeit_m_values[np.nonzero(
geschwindigkeit_m_values)].size == 0:
var_geschwindigkeit_m = -1
else:
var_geschwindigkeit_m = np.var(
geschwindigkeit_m_values[np.nonzero(geschwindigkeit_m_values)])
header = np.append(header, [
versuch_name + '_varianz_delta_l', versuch_name + '_varianz_delta_r',
versuch_name + '_varianz_delta_m',
versuch_name + '_varianz_geschwindigkeit_l',
versuch_name + '_varianz_geschwindigkeit_r',
versuch_name + '_varianz_geschwindigkeit_m'
])
# Tendenz auswerten
condition_voraus_l = np.equal(tendenz_l_values, 1)
num_voraus_l = len(np.extract(condition_voraus_l, tendenz_l_values))
condition_voraus_r = np.equal(tendenz_r_values, 1)
num_voraus_r = len(np.extract(condition_voraus_r, tendenz_r_values))
condition_voraus_m = np.equal(tendenz_m_values, 1)
num_voraus_m = len(np.extract(condition_voraus_m, tendenz_m_values))
condition_hinter_l = np.equal(tendenz_l_values, -1)
num_hinter_l = len(np.extract(condition_hinter_l, tendenz_l_values))
condition_hinter_r = np.equal(tendenz_r_values, -1)
num_hinter_r = len(np.extract(condition_hinter_r, tendenz_r_values))
condition_hinter_m = np.equal(tendenz_m_values, -1)
num_hinter_m = len(np.extract(condition_hinter_m, tendenz_m_values))
# -100 steht fuer keinen errechneten Wert, sondern fuer nich vorhanden.
if num_voraus_l == 0 and num_hinter_l == 0:
tendenz_l = -100
else:
if num_voraus_l > num_hinter_l:
tendenz_l = 1
else:
if num_hinter_l > num_voraus_l:
tendenz_l = -1
else:
tendenz_l = 0
if num_voraus_r == 0 and num_hinter_r == 0:
tendenz_r = -100
else:
if num_voraus_r > num_hinter_r:
tendenz_r = 1
else:
if num_hinter_r > num_voraus_r:
tendenz_r = -1
else:
tendenz_r = 0
if num_voraus_m == 0 and num_hinter_m == 0:
tendenz_m = -100
else:
if num_voraus_m > num_hinter_m:
tendenz_m = 1
else:
if num_hinter_m > num_voraus_m:
tendenz_m = -1
else:
tendenz_m = 0
header = np.append(header, [
versuch_name + '_tendenz_l', versuch_name + '_tendenz_r',
versuch_name + '_tendenz_m'
])
# Berechnung der Kovarianz vom linken und rechten Auge
cov_x = np.cov(blick_l_x_values, blick_r_x_values)[0][1]
cov_y = np.cov(blick_l_y_values, blick_r_y_values)[0][1]
header = np.append(header, [
versuch_name + '_Kovarianz_blick_x',
versuch_name + '_Kovarianz_blick_y'
])
verhaeltnis_l_x_da = blick_l_x_values[np.nonzero(
blick_l_x_values)].size / blick_l_x_values.size
verhaeltnis_l_y_da = blick_l_y_values[np.nonzero(
blick_l_y_values)].size / blick_l_y_values.size
verhaeltnis_r_x_da = blick_r_x_values[np.nonzero(
blick_r_x_values)].size / blick_r_x_values.size
verhaeltnis_r_y_da = blick_r_y_values[np.nonzero(
blick_r_y_values)].size / blick_r_y_values.size
sacc_m = np.sum(sacc_m_values)
sacc_l = np.sum(sacc_l_values)
sacc_r = np.sum(sacc_r_values)
if versuch_name == 'Horizontal' or versuch_name == 'Liegende_8_schnell':
sacc_rate_m = sacc_m / (999 * 4)
sacc_rate_l = sacc_l / (999 * 4)
sacc_rate_r = sacc_r / (999 * 4)
else:
sacc_rate_m = sacc_m / (999 * 5)
sacc_rate_l = sacc_l / (999 * 5)
sacc_rate_r = sacc_r / (999 * 5)
header = np.append(header, [
versuch_name + '_links_verhaeltnis_x', versuch_name +
'_links_verhaeltnis_y', versuch_name + '_rechts_verhaeltnis_x',
versuch_name + '_rechts_verhaeltnis_y', versuch_name + '_sacc_m',
versuch_name + '_sacc_rate_m', versuch_name + '_sacc_l', versuch_name +
'_sacc_rate_l', versuch_name + '_sacc_r', versuch_name + '_sacc_rate_r'
])
yield [[
mean_delta_l, mean_delta_r, mean_delta_m, mean_geschwindigkeit_l,
mean_geschwindigkeit_r, mean_geschwindigkeit_m, max_delta_l,
max_delta_r, max_delta_m, max_geschwindigkeit_l, max_geschwindigkeit_r,
max_geschwindigkeit_m, min_delta_l, min_delta_r, min_delta_m,
min_geschwindigkeit_l, min_geschwindigkeit_r, min_geschwindigkeit_m,
std_delta_l, std_delta_r, std_delta_m, std_geschwindigkeit_l,
std_geschwindigkeit_r, std_geschwindigkeit_m, var_delta_l, var_delta_r,
var_delta_m, var_geschwindigkeit_l, var_geschwindigkeit_r,
var_geschwindigkeit_m, tendenz_l, tendenz_r, tendenz_m, cov_x, cov_y,
verhaeltnis_l_x_da, verhaeltnis_l_y_da, verhaeltnis_r_x_da,
verhaeltnis_r_y_da, sacc_m, sacc_rate_m, sacc_l, sacc_rate_l, sacc_r,
sacc_rate_r
]]
yield header
def _update_mean_variance(n_past, mu, var, X, sample_weight=None):
"""Compute online update of Gaussian mean and variance.
Given starting sample count, mean, and variance, a new set of
points X, and optionally sample weights, return the updated mean and
variance. (NB - each dimension (column) in X is treated as independent
-- you get variance, not covariance).
Can take scalar mean and variance, or vector mean and variance to
simultaneously update a number of independent Gaussians.
See Stanford CS tech report STAN-CS-79-773 by Chan, Golub, and LeVeque:
http://i.stanford.edu/pub/cstr/reports/cs/tr/79/773/CS-TR-79-773.pdf
Parameters
----------
n_past : int
Number of samples represented in old mean and variance. If sample
weights were given, this should contain the sum of sample
weights represented in old mean and variance.
mu : array-like, shape (number of Gaussians,)
Means for Gaussians in original set.
var : array-like, shape (number of Gaussians,)
Variances for Gaussians in original set.
sample_weight : array-like, shape (n_samples,), optional
Weights applied to individual samples (1. for unweighted).
Returns
-------
total_mu : array-like, shape (number of Gaussians,)
Updated mean for each Gaussian over the combined set.
total_var : array-like, shape (number of Gaussians,)
Updated variance for each Gaussian over the combined set.
"""
if X.shape[0] == 0:
return mu, var
# Compute (potentially weighted) mean and variance of new datapoints
if sample_weight is not None:
n_new = float(sample_weight.sum())
new_mu = np.average(X, axis=0, weights=sample_weight / n_new)
new_var = np.average((X - new_mu) ** 2, axis=0,
weights=sample_weight / n_new)
else:
n_new = X.shape[0]
new_var = np.var(X, axis=0)
new_mu = np.mean(X, axis=0)
if n_past == 0:
return new_mu, new_var
n_total = float(n_past + n_new)
# Combine mean of old and new data, taking into consideration
# (weighted) number of observations
total_mu = (n_new * new_mu + n_past * mu) / n_total
# Combine variance of old and new data, taking into consideration
# (weighted) number of observations. This is achieved by combining
# the sum-of-squared-differences (ssd)
old_ssd = n_past * var
new_ssd = n_new * new_var
total_ssd = (old_ssd + new_ssd +
(n_past / float(n_new * n_total)) *
(n_new * mu - n_new * new_mu) ** 2)
total_var = total_ssd / n_total
return total_mu, total_var
def SNR (self, y):
return self.P_0 - np.var(y)