add subclass for quantile regression.

lppls/lppls_q.py

@@ -0,0 +1,36 @@
+from lppls.lppls import LPPLS
+import numpy as np
+
+
+class QLPPLS(LPPLS):
+    def __init__(self, observations, q=0.5):
+        super().__init__(observations)
+        self.q = q
+
+    def func_restricted(self, x, *args):
+        """
+        Finds the least absolute differences adjusted for the q-dependent loss function.
+        Args:
+            x(np.ndarray):  1-D array with shape (n,).
+            args: Tuple of the fixed parameters needed to completely specify the function.
+        Returns:
+            (float)
+        """
+
+        tc = x[0]
+        m = x[1]
+        w = x[2]
+        observations = args[0]
+
+        rM = self.matrix_equation(observations, tc, m, w)
+        a, b, c1, c2 = rM[:, 0].tolist()
+
+        delta = [self.lppls(t, tc, m, w, a, b, c1, c2) for t in observations[0, :]]
+        delta = np.subtract(delta, observations[1, :])
+
+        # Use the L1 norm (sum of absolute differences) instead of the L2 norm
+        # Apply the q-dependent loss function using the given quantile
+        loss = np.sum([-(1 - self.q) * e if e < 0 else self.q * e for e in np.abs(delta)])
+
+        return loss
+