datasets = ModelSelectUtils.getRegressionDatasets(True)
gammas = numpy.unique(numpy.array(numpy.round(2 ** numpy.arange(1, 7.25, 0.25) - 1), dtype=numpy.int))
print(gammas)
# To use the betas in practice, pick the lowest value so far
for datasetName, numRealisations in datasets:
try:
A = numpy.load(outputDir + datasetName + "Beta.npz")["arr_0"]
inds = gammas > 10
tempGamma = numpy.sqrt(gammas[inds])
tempA = A[inds, :]
tempA = numpy.clip(tempA, 0, 1)
plt.figure(0)
plt.plot(tempGamma, Util.cumMin(tempA[:, 0]), label="50")
plt.plot(tempGamma, Util.cumMin(tempA[:, 1]), label="100")
plt.plot(tempGamma, Util.cumMin(tempA[:, 2]), label="200")
plt.legend()
plt.title(datasetName)
plt.xlabel("gamma")
plt.ylabel("Beta")
plt.show()
except:
print("Dataset not found " + datasetName)
