- refacored state

helper.py

@@ -0,0 +1,86 @@
+import numpy as np
+from numpy.random import uniform
+
+
+def create_test_state() -> np.array:
+    return np.array([['1', '2', '3'], ['4', '5', '6'], ['7', '8', '9']])
+
+
+def create_empty_state() -> np.array:
+    return np.array([['-', '-', '-'], ['-', '-', '-'], ['-', '-', '-']])
+
+
+def softmax(values: np.array, eps=1.0E-6) -> np.array:
+    result = (values + eps) / (sum(values + eps))
+    return result
+
+
+def sample(values: np.array, with_debug=False):
+    # Normalize and sort
+    probs = softmax(values)
+    sorted_indices = np.argsort(probs)
+    sorted_probs = probs[sorted_indices]
+    z = uniform()
+    if with_debug:
+        print(f"Probs={probs}")
+        print(f"Z={z:.3f}")
+    index = None
+    p_sum = 0
+    for idx, p in enumerate(sorted_probs):
+        p_sum += p
+        if z <= p_sum:
+            index = sorted_indices[idx]
+            break
+
+    return index
+
+
+def test_sample(data):
+    p = np.array(data)
+    c = np.array([0]*len(data))
+
+    for i in range(0, 1000):
+        index = sample(p, with_debug=False)
+        c[index] += 1
+
+    print(c)
+
+
+def to_state_string(state, state_nex=None):
+    sp = '      '
+    sp_arrow = '  =>  '
+    sp_ = [sp, sp_arrow, sp]
+
+    def col(str_in, state):
+        str_out = str_in
+        for c in range(0, 3):
+            char = state[r][c]
+            if char == '-':
+                char = ' '
+            str_out += "|" + char
+        str_out += '|'
+        return str_out
+
+    state_str = ''
+    for r in range(0, 3):
+        if state is not None:
+            state_str = col(state_str, state)
+
+        if state_nex is not None:
+            state_str += sp_[r]
+            state_str = col(state_str, state_nex)
+
+        if r != 2:
+            state_str += '\x0A'
+
+    return state_str
+
+print("Testing")
+test_sample([0.7, 0.1, 0.1, 0.1])
+test_sample([0.1, 0.1, 0.2, 0.1])
+test_sample([0.2, 0.8])
+test_sample([0.1, 0.1])
+test_sample([0.3, 0.0, 0.2, 0.0])
+test_sample([0.0, 0.0, 0.0, 0.0])
+test_sample([0.5, 0.0, 0.2, 0.3])
+test_sample([0.3, 0.1, 0.4, 0.2])