def test_qrnn(self, backend):
"""
Test training of QRNNs using numpy arrays as input.
"""
set_backend(backend)
qrnn = QRNN(self.x_train.shape[1], np.linspace(0.05, 0.95, 10))
qrnn.train((self.x_train, self.y_train), maximum_epochs=1)
qrnn.predict(self.x_train)
x, qs = qrnn.cdf(self.x_train[:2, :])
assert qs[0] == 0.0
assert qs[-1] == 1.0
x, y = qrnn.pdf(self.x_train[:2, :])
assert x.shape == y.shape
mu = qrnn.posterior_mean(self.x_train[:2, :])
assert len(mu.shape) == 1
r = qrnn.sample_posterior(self.x_train[:4, :], n=2)
assert r.shape == (4, 2)
r = qrnn.sample_posterior_gaussian_fit(self.x_train[:4, :], n=2)
assert r.shape == (4, 2)
def test_qrnn_datasets(self, backend):
"""
Provide data as dataset object instead of numpy arrays.
"""
set_backend(backend)
backend = get_backend(backend)
data = backend.BatchedDataset((self.x_train, self.y_train), 256)
qrnn = QRNN(self.x_train.shape[1], np.linspace(0.05, 0.95, 10))
qrnn.train(data, maximum_epochs=1)
def test_save_qrnn(self, backend):
"""
Test saving and loading of QRNNs.
"""
set_backend(backend)
qrnn = QRNN(self.x_train.shape[1], np.linspace(0.05, 0.95, 10))
f = tempfile.NamedTemporaryFile()
qrnn.save(f.name)
qrnn_loaded = QRNN.load(f.name)
x_pred = qrnn.predict(self.x_train)
x_pred_loaded = qrnn.predict(self.x_train)
if not type(x_pred) == np.ndarray:
x_pred = x_pred.detach()
assert np.allclose(x_pred, x_pred_loaded)
# -*- coding: utf-8 -*-
"""
Created on Sat Sep 12 22:09:27 2020
@author: inderpreet
PLot the uncertainties in randomly chosen cases
"""
import matplotlib.pyplot as plt
import numpy as np
import netCDF4
from matplotlib.ticker import (MultipleLocator, FormatStrFormatter,
AutoMinorLocator)
from typhon.retrieval.qrnn import set_backend, QRNN
set_backend("pytorch")
import stats as S
from ici import iciData
from calibration import calibration
import random
plt.rcParams.update({'font.size': 26})
from matplotlib import colors as mcolors
colors = dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS)
#%% input parameters
depth = 4
width = 128
quantiles = np.array([0.002, 0.03, 0.16, 0.5, 0.84, 0.97, 0.998])
batchSize = 128