add get_weights port

src/get_weights.py

@@ -0,0 +1,80 @@
+import numpy as np
+from itertools import product
+
+class WildDrawFunctionException(Exception):
+    pass
+
+def rademacher(n: int) -> np.ndarray:
+    rng = np.random.default_rng()
+    return rng.choice([-1,1],size=n, replace=True)
+
+def mammen(n: int) -> np.ndarray:
+    rng = np.random.default_rng()
+    return rng.choice(
+        a= np.array([-1, 1]) * (np.sqrt(5) + np.array([-1, 1])) / 2, #TODO: #10 Should this divide the whole expression by 2 or just the second part
+        size=n,
+        replace=True,
+        p = (np.sqrt(5) + np.array([1, -1])) / (2 * np.sqrt(5))
+    )
+
+def norm(n):
+    rng = np.random.default_rng()
+    return rng.normal(size=n)
+
+def webb(n):
+    rng = np.random.default_rng()
+    return rng.choice(
+        a = np.concatenate([-np.sqrt(np.array([3,2,1]) / 2), np.sqrt(np.array([1,2,3]) / 2)]),
+        replace=True,
+        size=n
+    )
+
+wild_draw_fun_dict = {
+    'rademacher' : rademacher,
+    'mammen' : mammen,
+    'norm' : norm,
+    'webb' : webb
+}
+
+
+def get_weights(t : str, full_enumeration: bool, N_G_bootcluster: int, boot_iter: int) -> np.ndarray:
+    """draw bootstrap weights
+
+    Args:
+        t (str): the type of the weights distribution. Either 'rademacher', 'mammen', 'norm' or 'webb'
+        full_enumeration (bool): should deterministic full enumeration be employed
+        N_G_bootcluster (int): the number of bootstrap clusters
+        boot_iter (int): the number of bootstrap iterations
+
+    Returns:
+        np.ndarray: a matrix of dimension N_G_bootcluster x (boot_iter + 1)
+    """    
+
+    #TODO: we can use the `case` feature in python, but that's only available in 3.10+ will do a 3.7 version for now
+    # Will take out this and make separate functions for readability
+
+    wild_draw_fun = wild_draw_fun_dict.get(t)
+
+    if wild_draw_fun is None:
+        raise WildDrawFunctionException("Function type specified is not supported or there is a typo.")
+
+    # do full enumeration for rademacher weights if bootstrap iterations
+    # B exceed number of possible permutations else random sampling
+
+    # full_enumeration only for rademacher weights (set earlier)
+    if full_enumeration: 
+        t = 0 # what is this needed for? 
+        # with N_G_bootcluster draws, get all combinations of [-1,1] WITH 
+        # replacement, in matrix form
+        v0 = np.transpose(np.array(list(product([-1,1], repeat=N_G_bootcluster))))
+    else:
+        # else: just draw with replacement - by chance, some permutations
+        # might occur more than once
+        v0 = wild_draw_fun(n = N_G_bootcluster * boot_iter)
+        v0.reshape(N_G_bootcluster, boot_iter) # weights matrix
+
+    v = np.insert(v0, 0, 1,axis = 1)
+
+    return v
+
+