From 3ffc225f798a1d019b941475fbab28223112b139 Mon Sep 17 00:00:00 2001
From: Aleksandr Michuda <amichuda@gmail.com>
Date: Wed, 12 Oct 2022 11:50:12 -0400
Subject: [PATCH 1/2] add get_weights port

---
 src/get_weights.py | 84 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 84 insertions(+)
 create mode 100644 src/get_weights.py

diff --git a/src/get_weights.py b/src/get_weights.py
new file mode 100644
index 0000000..4e19401
--- /dev/null
+++ b/src/get_weights.py
@@ -0,0 +1,84 @@
+import numpy as np
+from itertools import permutations
+
+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.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
+        #TODO: #12 Is this just a permutation function?
+        # gtools_permutations(
+        v0 = permutations( 
+            [-1,1],
+            r = N_G_bootcluster,
+        )
+        v = np.insert(v0, 0, 1)
+    else:
+        # else: just draw with replacement - by chance, some permutations
+        # might occur more than once
+        v = wild_draw_fun(n = N_G_bootcluster * (boot_iter + 1))
+        #dim(v) <- c(N_G_bootcluster, boot_iter +1) don't think we need this
+        v = np.insert(v, 0, 1)
+        # v[, 1] <- 1
+    
+    return v
+  
+  

From dee43a10167d57280ce7a82372acaaa83da29255 Mon Sep 17 00:00:00 2001
From: Alexander Fischer <alexander-fischer1801@t-online.de>
Date: Fri, 14 Oct 2022 14:59:35 +0200
Subject: [PATCH 2/2] fix minor error in webb weights, replace permutations()
 by product(), initiate full weights matrix at once

---
 src/get_weights.py | 26 +++++++++++---------------
 1 file changed, 11 insertions(+), 15 deletions(-)

diff --git a/src/get_weights.py b/src/get_weights.py
index 4e19401..a755e12 100644
--- a/src/get_weights.py
+++ b/src/get_weights.py
@@ -1,5 +1,5 @@
 import numpy as np
-from itertools import permutations
+from itertools import product
 
 class WildDrawFunctionException(Exception):
     pass
@@ -24,7 +24,7 @@ def norm(n):
 def webb(n):
     rng = np.random.default_rng()
     return rng.choice(
-        a = [-np.sqrt(np.array([3,2,1]) / 2), np.sqrt(np.array([1,2,3]) / 2)],
+        a = np.concatenate([-np.sqrt(np.array([3,2,1]) / 2), np.sqrt(np.array([1,2,3]) / 2)]),
         replace=True,
         size=n
     )
@@ -63,22 +63,18 @@ def get_weights(t : str, full_enumeration: bool, N_G_bootcluster: int, boot_iter
 
     # full_enumeration only for rademacher weights (set earlier)
     if full_enumeration: 
-        t = 0
-        #TODO: #12 Is this just a permutation function?
-        # gtools_permutations(
-        v0 = permutations( 
-            [-1,1],
-            r = N_G_bootcluster,
-        )
-        v = np.insert(v0, 0, 1)
+        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
-        v = wild_draw_fun(n = N_G_bootcluster * (boot_iter + 1))
-        #dim(v) <- c(N_G_bootcluster, boot_iter +1) don't think we need this
-        v = np.insert(v, 0, 1)
-        # v[, 1] <- 1
-    
+        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