def tKrig():
'''
Kriging test
'''
Loc, POIC, Prec = DataLoad.lcsv('TestData\GaugeLoc.csv',
'TestData\InterpPts.csv',
'TestData\Dataset.csv')
Loc = numpy.array(Loc)/1000.0
POIC = numpy.array(POIC)/1000.0
SVExp, CovMea = Kriging.exp_semivariogram(Prec, Loc)
xopt, ModOpt, VarFunArr = Kriging.theor_variogram(SVExp)
Z, SP, ZAvg = Kriging.Krig(10.0, POIC, Loc, Prec, CovMea, ModOpt, xopt,
VarFunArr,10 ,11, 'Ord')
print Z
print ZAvg
return Z, ZAvg, CovMea
def animate(self, i):
Z, T, F, W, WD = DataHandler.propagation(
i, self.option, self.enteredWindSpeedValue,
self.enteredWindDirectionValue, self.enteredTemperatureValue,
self.enteredTrafficValue, self.enteredRainfallValue)
xMesh, yMesh, zPrediction, Zi = Kriging.execute(X, Y, Z, i)
self.fig.clf()
subplot = self.fig.add_subplot(111)
subplot.set_title(self.createTitle(self.option, i, T, F, W, WD))
subplContourf = subplot.imshow(backgroundImage, extent=[0, 90, 0, 60])
subplContourf = subplot.contourf(xMesh,
yMesh,
np.transpose(zPrediction),
50,
cmap=newCmap,
alpha=0.6,
vmin=-80,
vmax=190)
subplContourf = subplot.scatter(X,
Y,
c=Zi,
cmap=newCmap,
vmin=0,
vmax=80)
colorbar = self.fig.colorbar(subplContourf, fraction=0.03)
colorbar.set_label('Smog level')
cursor = mplcursors.cursor(subplContourf, hover=True)
cursor.connect(
"add",
lambda sel: sel.annotation.set_text(pointNames[sel.target.index]))
if i == self.numberOfMeasurements - 1:
self.animation = None
self.pauseButton.config(text="Show again")
self.dailySimButton["state"] = "normal"
self.weeklySimButton["state"] = "normal"
pre = Preprocess(all_dat='../all_games.pkl', pca_model='../eco_full_pca.pkl')
X, y = pre.ready_player_one(2)
scale = MinMaxScaler((-1.,1.))
X = scale.fit_transform(X)
from tqdm import tqdm
file_address = 'p2_bfgs_sigma_alpha8.286TRUNCATED.json'
with open(file_address, 'r') as f:
best_obj, best_sig = json.load( f)
f.close()
# unit_sig = np.ones(31)
# bounds = np.array(31*[[-1., 1.]])
# bestKrig = Kriging(best_sig, bounds=bounds)
# bestKrig.fit(X,y)
bounds = np.array(30*[[-1., 1.]])
num_ini_guess = 2
kriging_model = Kriging(best_sig, bounds=bounds, num_ini_guess=num_ini_guess)
with Model() as model: # model specifications in PyMC3 are wrapped in a with-statement
# Define likelihood
likelihood = kriging_model.obj(l,a)
with model:
start = {'Intercept_logodds': np.array(-1.4488360894175776),
'sigma_log': np.array(-0.7958719902826098),
'x_logodds': np.array(-0.3564261325183015)}
step = NUTS(scaling=start) # Instantiate MCMC sampling algorithm
trace = sample(2000, step, start=start, progressbar=True) # draw 2000 posterior samples using NUTS sampling
from tqdm import tqdm
file_address = 'p2_bfgs_sigma_alpha8.286TRUNCATED.json'
with open(file_address, 'r') as f:
best_obj, best_sig = json.load(f)
f.close()
# unit_sig = np.ones(31)
# bounds = np.array(31*[[-1., 1.]])
# bestKrig = Kriging(best_sig, bounds=bounds)
# bestKrig.fit(X,y)
bounds = np.array(30 * [[-1., 1.]])
num_ini_guess = 2
kriging_model = Kriging(best_sig, bounds=bounds, num_ini_guess=num_ini_guess)
with Model(
) as model: # model specifications in PyMC3 are wrapped in a with-statement
# Define likelihood
likelihood = kriging_model.obj(l, a)
with model:
start = {
'Intercept_logodds': np.array(-1.4488360894175776),
'sigma_log': np.array(-0.7958719902826098),
'x_logodds': np.array(-0.3564261325183015)
}
step = NUTS(scaling=start) # Instantiate MCMC sampling algorithm
trace = sample(
2000, step, start=start,