Delete traj_measure.py

traj_measure.py

@@ -1,104 +0,0 @@
-import torch
-import copy
-
-
-class Measure:
-    def sample(self, samples=100000, varn=2, points=100):
-        # Must be overridden
-        pass
-
-
-class BaseMeasure(Measure):
-    def __init__(
-        self, mu0=0.0, sigma0=1.0, mu1=0.0, sigma1=1.0, q=0.1, q0=0.5, device="cpu"
-    ):
-        """
-
-        Parameters
-        ----------
-        mu0 : mean of normal distribution of initial state, optional
-            The default is 0.0.
-        sigma0 : standard deviation of normal distribution of initial state, optional
-            The default is 1.0.
-        mu1 : DOUBLE, optional
-            mean of normal distribution of total variation. The default is 0.0.
-        sigma1 : standard deviation of normal distribution of total variation, optional
-            The default is 1.0.
-        q : DOUBLE, optional
-            probability of change of sign in derivative. The default is 0.1.
-        q0 : DOUBLE, optional
-            probability of initial sign of  derivative. The default is 0.5.
-        device : 'cpu' or 'cuda', optional
-            device on which to run the algorithm. The default is 'cpu'.
-
-        Returns
-        -------
-        None.
-
-        """
-        self.mu0 = mu0
-        self.sigma0 = sigma0
-        self.mu1 = mu1
-        self.sigma1 = sigma1
-        self.q = q
-        self.q0 = q0
-        self.device = device
-
-    def sample(self, samples=100000, varn=2, points=100):
-        """
-        Samples a set of trajectories from the basic measure space, with parameters
-        passed to the sampler
-
-        Parameters
-        ----------
-        points : INT, optional
-            number of points per trajectory, including initial one. The default is 1000.
-        samples : INT, optional
-            number of trajectories. The default is 100000.
-        varn : INT, optional
-            number of variables per trajectory. The default is 2.
-
-
-        Returns
-        -------
-        signal : samples x varn x points double pytorch tensor
-            The sampled signals.
-
-        """
-        if self.device == "cuda" and not torch.cuda.is_available():
-            raise RuntimeError("GPU card or CUDA library not available!")
-
-        # generate unif RN
-        signal = torch.rand(samples, varn, points, device=self.device)
-        # first point is special - set to zero for the moment, and set one point to 1
-        signal[:, :, 0] = 0.0
-        signal[:, :, -1] = 1.0
-        # sorting each trajectory
-        signal, _ = torch.sort(signal, 2)
-        # computing increments and storing them in points 1 to end
-        signal[:, :, 1:] = signal[:, :, 1:] - signal[:, :, :-1]
-        # generate initial state, according to a normal distribution
-        signal[:, :, 0] = self.mu0 + self.sigma0 * torch.randn(signal[:, :, 0].size())
-
-        # sampling change signs from bernoulli in -1, 1
-        derivs = (1 - self.q) * torch.ones(samples, varn, points, device=self.device)
-        derivs = 2 * torch.bernoulli(derivs) - 1
-        # sampling initial derivative
-        derivs[:, :, 0] = self.q0
-        derivs[:, :, 0] = 2 * torch.bernoulli(derivs[:, :, 0]) - 1
-        # taking the cumulative product along axis 2
-        derivs = torch.cumprod(derivs, 2)
-
-        # sampling total variation
-        totvar = torch.pow(
-            self.mu1 + self.sigma1 * torch.randn(samples, varn, 1, device=self.device),
-            2,
-         )
-        # multiplying total variation and derivatives and making initial point non-invasive
-        derivs = derivs * totvar
-        derivs[:, :, 0] = 1.0
-
-        # computing trajectories by multiplying and then doing a cumulative sum
-        signal = signal * derivs
-        signal = torch.cumsum(signal, 2)
-        return signal