updated metadata output

analytics_helper_functions.py

@@ -7,6 +7,9 @@ def get_num_letters_excess(words, board_size=15):
 def get_mean_word_length(words):
     return np.mean([len(word) for word in words])
 
+def get_max_word_length(words):
+    return np.max([len(word) for word in words])
+
 def get_num_overlaps_exact(w1, w2):
     overlaps = 0
     # Assume that w1 always has the same fixed orientation

make_puzzle_v2.py

@@ -13,55 +13,20 @@
 import warnings
 
 # TODO: CLEAN UP THIS MESS OF A FUNCTION
-def get_word_set_stats(words):
+def get_word_set_stats(words, board_size):
     board_words = [w.board for w in words]
+    collisions_avoidance_matrix = np.array(ahf.get_collision_avoidance_probability_pairwise(board_words, board_size))
+    tril_inds = np.tril_indices(len(words), k=-1)
 
     num_words = len(board_words)
     letter_excess = ahf.get_num_letters_excess(board_words)
     mean_len = ahf.get_mean_word_length(board_words)
-    total_overlaps = ahf.get_num_overlaps_total(board_words)
+    max_len = ahf.get_max_word_length(board_words)
 
-    return num_words, letter_excess, mean_len, total_overlaps
+    min_collision_avoidance_prob = collisions_avoidance_matrix[tril_inds].min()
+    mean_collision_avoidance_prob = collisions_avoidance_matrix[tril_inds].mean()
 
-# REPLACE WITH WORD-LENGTH NORMALIZED OVERLAP MEASURES
-def enrich_word_set(chosen_word_tuples, extra_word_tuples, max_mean_word_len=5.5):
-
-    num_words, letter_excess, mean_len, total_overlaps = get_word_set_stats(chosen_word_tuples)
-    print(f"\nInitial stats: num_words={num_words}, letter_excess={letter_excess:.2f}, mean_len={mean_len:.2f}, total_overlaps={total_overlaps}")
-
-    # Rather than hard-coding a static max_mean_word_len, make it a function of what we started with
-    # max_mean_word_len = 1.1 * mean_len
-
-    while True:
-        # Find least-overlapping word in the set
-
-        per_word_overlaps = [ahf.get_num_overlaps_per_word(wt.board, [wt.board for wt in chosen_word_tuples]) for wt in chosen_word_tuples]
-        least_overlapping_word_idx = np.argmin(per_word_overlaps)
-        least_overlapping_word = chosen_word_tuples[least_overlapping_word_idx]
-
-        chosen_word_tuples = chosen_word_tuples[:least_overlapping_word_idx] + chosen_word_tuples[least_overlapping_word_idx+1:]
-        extra_word_tuples = [least_overlapping_word] + extra_word_tuples
-
-        per_word_overlaps = [ahf.get_num_overlaps_per_word(wt.board, [wt.board for wt in chosen_word_tuples]) for wt in extra_word_tuples]
-        most_overlapping_word_idx = np.argmax(per_word_overlaps)
-        most_overlapping_word = extra_word_tuples[most_overlapping_word_idx]
-
-        chosen_word_tuples = chosen_word_tuples + [most_overlapping_word]
-        extra_word_tuples = extra_word_tuples[:most_overlapping_word_idx] + extra_word_tuples[most_overlapping_word_idx+1:]
-
-        num_words, letter_excess, mean_len, total_overlaps = get_word_set_stats(chosen_word_tuples)
-        print(f"\nRemoved: '{least_overlapping_word.pretty}', Added: '{most_overlapping_word.pretty}'")
-        print(f"Enriched stats: num_words={num_words}, letter_excess={letter_excess:.2f}, mean_len={mean_len:.2f}, total_overlaps={total_overlaps}")
-
-        if least_overlapping_word == most_overlapping_word:
-            print("\nRedundant transposition --> break")
-            break
-
-        if mean_len > max_mean_word_len:
-            print("\nPassed max_mean_word_len --> break")
-            break
-
-    return chosen_word_tuples
+    return num_words, letter_excess, mean_len, max_len, min_collision_avoidance_prob, mean_collision_avoidance_prob
 
 
 def get_words_for_board(word_tuples, board_size, packing_constant=1.1, max_word_len=8):
@@ -85,15 +50,12 @@ def get_words_for_board(word_tuples, board_size, packing_constant=1.1, max_word_
     if not num_words:
         raise ValueError(f"Too few semantic neighbor words to pack a {board_size}x{board_size} board.")
 
-    # chosen_word_tuples = enrich_word_set(word_tuples[:num_words], word_tuples[num_words:])
-    # return sorted(chosen_word_tuples, key=lambda wt: len(wt.board))
-
-    num_words, packing_level, mean_word_len, total_overlap = get_word_set_stats(word_tuples[:num_words])
+    num_words, packing_level, mean_word_len, max_word_len, min_collision_avoidance_prob, mean_collision_avoidance_prob = get_word_set_stats(word_tuples[:num_words], board_size)
     print("\nWord stats:")
     print(f"    num_words: {num_words}")
     print(f"    packing_level: {packing_level:.3f}")
-    print(f"    mean_word_len: {mean_word_len:.2f}")
-    print(f"    total_overlap: {total_overlap}")
+    print(f"    word_len (mean/max): {mean_word_len:.2f} / {max_word_len}")
+    print(f"    collision_avoidance_prob (min/mean): {min_collision_avoidance_prob:.6f} / {mean_collision_avoidance_prob:.6f}")
 
     return word_tuples[:num_words]
 
@@ -111,12 +73,12 @@ def get_words_for_board_optimize(word_tuples, board_size, packing_constant=1.1):
     max_word_tuple_idx_naive = (np.cumsum([len(wt.board) for wt in word_tuples]) < packing_constant * board_size**2).sum()
     word_tuples_naive = word_tuples[:max_word_tuple_idx_naive]
 
-    num_words, packing_level, mean_word_len, total_overlap = get_word_set_stats(word_tuples_naive)
+    num_words, packing_level, mean_word_len, max_word_len, min_collision_avoidance_prob, mean_collision_avoidance_prob = get_word_set_stats(word_tuples_naive, board_size)
     print("\nPre-optimization word stats:")
     print(f"    num_words: {num_words}")
     print(f"    packing_level: {packing_level:.3f}")
-    print(f"    mean_word_len: {mean_word_len:.2f}")
-    print(f"    total_overlap: {total_overlap}")
+    print(f"    word_len (mean/max): {mean_word_len:.2f} / {max_word_len}")
+    print(f"    collision_avoidance_prob (min/mean): {min_collision_avoidance_prob:.6f} / {mean_collision_avoidance_prob:.6f}")
 
     # Run the script
     p = subprocess.Popen(["/Applications/MiniZincIDE.app/Contents/Resources/minizinc", "--solver", "Chuffed", "--all-solutions", "MiniZinc_scripts/find_dense_overlap_word_subset.mzn", "tmp/pre_data.dzn"], stdout=subprocess.PIPE)
@@ -144,12 +106,12 @@ def get_words_for_board_optimize(word_tuples, board_size, packing_constant=1.1):
     #       NEED TO MAKE SURE IT'S DONE WRT SEMANITIC SIMILARITY
     #       STORE THIS INFORMATION WITHIN THE WORD OBJECT
 
-    num_words, packing_level, mean_word_len, total_overlap = get_word_set_stats(word_tuples)
+    num_words, packing_level, mean_word_len, max_word_len, min_collision_avoidance_prob, mean_collision_avoidance_prob = get_word_set_stats(word_tuples, board_size)
     print("\nPost-optimization word stats:")
     print(f"    num_words: {num_words}")
     print(f"    packing_level: {packing_level:.3f}")
-    print(f"    mean_word_len: {mean_word_len:.2f}")
-    print(f"    total_overlap: {total_overlap}")
+    print(f"    word_len (mean/max): {mean_word_len:.2f} / {max_word_len}")
+    print(f"    collision_avoidance_prob (min/mean): {min_collision_avoidance_prob:.6f} / {mean_collision_avoidance_prob:.6f}")
 
     return word_tuples
 
@@ -226,6 +188,7 @@ def find_word_in_board(board, word):
     # Can be >1 location if the word was added to the board in multiple places.
     # Can ALSO happen if the word is a palindrome; this is a false positive case and should be removed.
     word_locations = []
+    word_deltas = []
     for y in range(len(board)):
         for x in range(len(board)):
             for dy in (-1,0,1):
@@ -243,7 +206,8 @@ def find_word_in_board(board, word):
                         new_loc = [(y+i*dy, x+i*dx) for i in range(len(word))]
                         if all(sorted(loc) != sorted(new_loc) for loc in word_locations):
                             word_locations.append(new_loc)
-    return word_locations
+                            word_deltas.append((dy,dx))
+    return word_locations, word_deltas
 
 
 def find_words_in_board(board, word_tuples):
@@ -258,7 +222,8 @@ def find_words_in_board(board, word_tuples):
 
     Return the words satisfying the different conditions
     """
-    word_locations_on_board =  [find_word_in_board(board, wt.board) for wt in word_tuples]
+    word_locations_on_board, word_deltas_on_board = zip(*[find_word_in_board(board, wt.board) for wt in word_tuples])
+    flattened_deltas = [d for deltas in word_deltas_on_board for d in deltas]
 
     covered_up_words = []
     doubled_up_words = []
@@ -278,7 +243,7 @@ def find_words_in_board(board, word_tuples):
                     w1_letter_positions_remaining = [pos for pos in w1_letter_positions_remaining if pos != loc]
             if not w1_letter_positions_remaining:
                 covered_up_words.append(wt)
-    return covered_up_words, doubled_up_words
+    return covered_up_words, doubled_up_words, flattened_deltas
 
 
 def make_puzzle(topic, board_size, packing_constant, strategy, optimize_words, relatedness_cutoff, n_proc=4):
@@ -356,7 +321,7 @@ def make_puzzle(topic, board_size, packing_constant, strategy, optimize_words, r
         # TODO FOR LATER: EDGE-CASE WHERE REMOVING ONE OVERLAPPING WORD RENDERS ANOTHER NON-OVERLAPPING
         # TODO FOR LATER: ANOTHER EDGE-CASE WHERE A WORD GETS ADDED WHEN THE HIDDEN LETTERS GET FILLED IN
         board = [line.strip().split() for line in raw_board.decode("utf-8").strip().split("\n")] # THIS IS REDUNDANT
-        covered_up_words, doubled_up_words = find_words_in_board(board, word_tuples_to_fit)
+        covered_up_words, doubled_up_words, deltas = find_words_in_board(board, word_tuples_to_fit)
         # If word appears multiple times, print a warning and try again
         if doubled_up_words:
             # warnings.warn(f"\nwords appear more than once on the board: {', '.join([wt.pretty for wt in doubled_up_words])}\nboard will be discarded and regenerated\n")
@@ -387,13 +352,16 @@ def make_puzzle(topic, board_size, packing_constant, strategy, optimize_words, r
     blank_locs = [(i,j) for i,row in enumerate(board) for j,letter in enumerate(row) if letter == "_"]
     # If number of blanks is exactly 0, no special word is needed
     if len(blank_locs) not in hidden_word_tuple_dict and len(blank_locs) != 0:
-        print(len(blank_locs), blank_locs)
-        print(hidden_word_tuple_dict)
-        raise ValueError("Number of remaining blanks does not fit any available word.")
+        raise ValueError(f"Number of remaining blanks does not fit any available word: {len(blank_locs)}")
     k = 0
     for i, j in blank_locs:
         board[i][j] = hidden_word_tuple_dict[len(blank_locs)].board[k]
         k += 1
+    delta_cntr = Counter(deltas)
+    print(f"\nhorizontal (fwd/bwd): {delta_cntr[(0,1)]}/{delta_cntr[(0,-1)]}")
+    print(f"vertical (fwd/bwd): {delta_cntr[(1,0)]}/{delta_cntr[(-1,0)]}")
+    print(f"diagonal du (fwd/bwd): {delta_cntr[(-1,1)]}/{delta_cntr[(1,-1)]}")
+    print(f"diagonal ud (fwd/bwd): {delta_cntr[(1,1)]}/{delta_cntr[(-1,-1)]}\n")
     # Print the topic above the board
     print(f"\nTopic:  {' / '.join(topic)}\n\n")
     for row in board:

other.rtf

@@ -5,24 +5,24 @@
 \margl1440\margr1440\vieww12820\viewh11240\viewkind0
 \pard\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\pardirnatural\qc\partightenfactor0
 
-\f0\fs32 \cf2 Topic:  colorful / colors\
+\f0\fs32 \cf2 Topic:  cuddle\
 \
 \
-C Y A N W O R B R O A N T R P\
-B O R Y E N O T I N C T I Y I\
-Y R G E F G Y E W T Y C N E N\
-H E A I L I N W T O H D T R E\
-S V U V D T R A O I L G U G E\
-A L S L E N T U H H H L I A R\
-L I E U B M I O A C S W E R G\
-F S E L P R U P M O T L E Y B\
-L E T I J E V T S L A M A L A\
-U P I G A D I O H O I T A Z G\
-S I N H Z D V U A U N C U U L\
-H A G T Z E I C D R K R I W O\
-D E E P Y N D H E N E S I A S\
-S H O T N I A P I D E U H R S\
-H U E S K I N P P Y A R G B C\
+P L O P R U L S T W I R L C O\
+T N N M M O S O B W A K E N E\
+I E E H C T U L C A V O R T C\
+B T V H T S E N U Z Z L E M A\
+A R I S S I K P U C K E R U R\
+H A L W O E M S W A D D L E B\
+O M S S C B R A I N W A S H M\
+C S P Y H A F F E C T I O N E\
+U Q O M E S O C I A L I Z E P\
+D U N P M L N H U G G I N G A\
+D E G A I E O I N I B B L E M\
+L E I T C E L P O E P R L T P\
+E Z N H A P U K E F L I R T E\
+I E G Y L E F O I S T S U U R\
+L O V E Z O D B A W L I C K P\
 \
 \
-aurify   azure   black   blue   brave   braw   bright   brown   change   colour   cyan   deep   flashy   flush   gaudy   gloss   gray   green   grey   guise   hued   hues   imbue   indigo   jazzy   light   motley   mottle   paint   pied   pink   purple   redden   rich   roan   sepia   shade   shot   showy   silver   skin   stain   tinct   tinge   tint   tone   touch   vivid   white   yellow}
+affection   bawl   bosom   brainwash   cavort   chemical   clutch   cohabit   cuddle   curl   doze   elope   embrace   flirt   foist   freshen   hugging   kiss   lick   liven   love   meow   nest   nibble   nuzzle   pamper   people   plop   pucker   puke   purr   sleep   slurp   smarten   socialize   sponging   squeeze   swaddle   sympathy   twirl   waken}

tmp/data1.dzn

@@ -1,9 +1,9 @@
 n = 15;
-m = 52;
+m = 44;
 
-max_len = 6;
-word_lens = [ 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6 ];
-words = [| P, I, E, D, E, E | B, R, A, W, E, E | D, E, E, P, E, E | R, O, A, N, E, E | S, K, I, N, E, E | P, I, N, K, E, E | B, L, U, E, E, E | C, Y, A, N, E, E | G, R, A, Y, E, E | R, I, C, H, E, E | T, O, N, E, E, E | T, I, N, T, E, E | S, H, O, T, E, E | H, U, E, S, E, E | H, U, E, D, E, E | G, R, E, Y, E, E | J, A, Z, Z, Y, E | A, Z, U, R, E, E | T, O, U, C, H, E | B, R, O, W, N, E | G, A, U, D, Y, E | B, R, A, V, E, E | G, L, O, S, S, E | G, R, E, E, N, E | P, A, I, N, T, E | I, M, B, U, E, E | S, H, A, D, E, E | S, T, A, I, N, E | F, L, U, S, H, E | S, E, P, I, A, E | W, H, I, T, E, E | B, L, A, C, K, E | T, I, N, G, E, E | G, U, I, S, E, E | L, I, G, H, T, E | S, H, O, W, Y, E | V, I, V, I, D, E | T, I, N, C, T, E | M, O, T, L, E, Y | C, H, R, O, M, A | M, O, T, T, L, E | S, I, L, V, E, R | R, E, D, D, E, N | B, R, I, G, H, T | C, O, L, O, U, R | P, U, R, P, L, E | A, U, R, I, F, Y | A, B, L, A, Z, E | I, N, D, I, G, O | C, H, A, N, G, E | F, L, A, S, H, Y | Y, E, L, L, O, W |];
+max_len = 7;
+word_lens = [ 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7 ];
+words = [| B, A, C, H, E, E, E | M, A, S, S, E, E, E | P, O, P, E, E, E, E | P, I, A, N, O, E, E | I, T, A, L, Y, E, E | O, P, E, R, A, E, E | M, U, S, I, C, E, E | L, I, S, Z, T, E, E | C, E, L, L, O, E, E | E, T, U, D, E, E, E | C, H, O, I, R, E, E | V, E, R, D, I, E, E | S, A, L, O, N, E, E | P, A, R, I, S, E, E | C, A, N, T, O, E, E | O, R, G, A, N, E, E | L, A, R, G, O, E, E | M, O, T, E, T, E, E | F, L, U, T, E, E, E | W, A, L, T, Z, E, E | H, A, Y, D, N, E, E | W, R, I, T, E, R, E | R, I, D, D, I, M, E | V, I, O, L, I, N, E | S, O, N, A, T, A, E | L, O, N, D, O, N, E | C, Z, E, C, H, S, E | M, U, N, I, C, H, E | P, R, A, G, U, E, E | T, E, C, H, N, O, E | V, I, E, N, N, A, E | B, R, A, H, M, S, E | W, A, R, S, A, W, E | H, A, N, D, E, L, E | W, A, G, N, E, R, E | M, E, L, O, D, Y, E | P, O, L, A, N, D, E | L, E, G, A, T, O, E | A, U, T, H, O, R, E | C, H, O, P, I, N, E | M, O, Z, A, R, T, E | F, R, A, N, C, E, E | M, A, H, L, E, R, E | M, A, Z, U, R, K, A |];
 
 pos_var_strat = first_fail;
 pos_val_strat = indomain_median;

tmp/data2.dzn

@@ -1,9 +1,9 @@
 n = 15;
-m = 52;
+m = 44;
 
-max_len = 6;
-word_lens = [ 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6 ];
-words = [| G, R, E, Y, E, E | P, I, N, K, E, E | T, O, N, E, E, E | B, L, U, E, E, E | S, K, I, N, E, E | H, U, E, D, E, E | H, U, E, S, E, E | S, H, O, T, E, E | R, O, A, N, E, E | T, I, N, T, E, E | B, R, A, W, E, E | D, E, E, P, E, E | G, R, A, Y, E, E | P, I, E, D, E, E | C, Y, A, N, E, E | R, I, C, H, E, E | J, A, Z, Z, Y, E | P, A, I, N, T, E | G, R, E, E, N, E | W, H, I, T, E, E | T, O, U, C, H, E | G, A, U, D, Y, E | F, L, U, S, H, E | T, I, N, C, T, E | S, T, A, I, N, E | B, R, A, V, E, E | V, I, V, I, D, E | G, L, O, S, S, E | B, L, A, C, K, E | A, Z, U, R, E, E | S, H, A, D, E, E | S, H, O, W, Y, E | G, U, I, S, E, E | B, R, O, W, N, E | S, E, P, I, A, E | L, I, G, H, T, E | I, M, B, U, E, E | T, I, N, G, E, E | P, U, R, P, L, E | S, I, L, V, E, R | A, U, R, I, F, Y | I, N, D, I, G, O | C, O, L, O, U, R | B, R, I, G, H, T | A, B, L, A, Z, E | Y, E, L, L, O, W | C, H, A, N, G, E | M, O, T, L, E, Y | F, L, A, S, H, Y | R, E, D, D, E, N | C, H, R, O, M, A | M, O, T, T, L, E |];
+max_len = 7;
+word_lens = [ 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7 ];
+words = [| M, A, S, S, E, E, E | P, O, P, E, E, E, E | B, A, C, H, E, E, E | O, R, G, A, N, E, E | S, A, L, O, N, E, E | P, A, R, I, S, E, E | C, A, N, T, O, E, E | O, P, E, R, A, E, E | M, U, S, I, C, E, E | H, A, Y, D, N, E, E | L, A, R, G, O, E, E | V, E, R, D, I, E, E | W, A, L, T, Z, E, E | F, L, U, T, E, E, E | M, O, T, E, T, E, E | P, I, A, N, O, E, E | L, I, S, Z, T, E, E | E, T, U, D, E, E, E | I, T, A, L, Y, E, E | C, E, L, L, O, E, E | C, H, O, I, R, E, E | S, O, N, A, T, A, E | M, A, H, L, E, R, E | R, I, D, D, I, M, E | M, O, Z, A, R, T, E | L, O, N, D, O, N, E | F, R, A, N, C, E, E | W, A, R, S, A, W, E | V, I, E, N, N, A, E | L, E, G, A, T, O, E | C, Z, E, C, H, S, E | T, E, C, H, N, O, E | P, O, L, A, N, D, E | W, R, I, T, E, R, E | M, U, N, I, C, H, E | C, H, O, P, I, N, E | M, E, L, O, D, Y, E | A, U, T, H, O, R, E | H, A, N, D, E, L, E | V, I, O, L, I, N, E | W, A, G, N, E, R, E | B, R, A, H, M, S, E | P, R, A, G, U, E, E | M, A, Z, U, R, K, A |];
 
 pos_var_strat = first_fail;
 pos_val_strat = indomain_median;

tmp/data3.dzn

@@ -1,9 +1,9 @@
 n = 15;
-m = 52;
+m = 44;
 
-max_len = 6;
-word_lens = [ 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6 ];
-words = [| B, R, A, W, E, E | R, O, A, N, E, E | S, H, O, T, E, E | D, E, E, P, E, E | T, I, N, T, E, E | P, I, N, K, E, E | T, O, N, E, E, E | G, R, E, Y, E, E | B, L, U, E, E, E | G, R, A, Y, E, E | P, I, E, D, E, E | H, U, E, D, E, E | C, Y, A, N, E, E | S, K, I, N, E, E | R, I, C, H, E, E | H, U, E, S, E, E | A, Z, U, R, E, E | J, A, Z, Z, Y, E | W, H, I, T, E, E | F, L, U, S, H, E | S, H, A, D, E, E | L, I, G, H, T, E | P, A, I, N, T, E | T, I, N, C, T, E | S, E, P, I, A, E | B, R, O, W, N, E | G, A, U, D, Y, E | B, L, A, C, K, E | S, T, A, I, N, E | I, M, B, U, E, E | G, U, I, S, E, E | T, O, U, C, H, E | B, R, A, V, E, E | G, R, E, E, N, E | T, I, N, G, E, E | V, I, V, I, D, E | S, H, O, W, Y, E | G, L, O, S, S, E | I, N, D, I, G, O | S, I, L, V, E, R | A, B, L, A, Z, E | P, U, R, P, L, E | M, O, T, L, E, Y | C, H, R, O, M, A | M, O, T, T, L, E | C, O, L, O, U, R | A, U, R, I, F, Y | Y, E, L, L, O, W | R, E, D, D, E, N | C, H, A, N, G, E | B, R, I, G, H, T | F, L, A, S, H, Y |];
+max_len = 7;
+word_lens = [ 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7 ];
+words = [| M, A, S, S, E, E, E | B, A, C, H, E, E, E | P, O, P, E, E, E, E | P, I, A, N, O, E, E | S, A, L, O, N, E, E | C, H, O, I, R, E, E | P, A, R, I, S, E, E | L, I, S, Z, T, E, E | O, R, G, A, N, E, E | H, A, Y, D, N, E, E | E, T, U, D, E, E, E | M, U, S, I, C, E, E | C, E, L, L, O, E, E | F, L, U, T, E, E, E | I, T, A, L, Y, E, E | L, A, R, G, O, E, E | V, E, R, D, I, E, E | M, O, T, E, T, E, E | W, A, L, T, Z, E, E | O, P, E, R, A, E, E | C, A, N, T, O, E, E | B, R, A, H, M, S, E | M, O, Z, A, R, T, E | L, E, G, A, T, O, E | H, A, N, D, E, L, E | C, Z, E, C, H, S, E | L, O, N, D, O, N, E | C, H, O, P, I, N, E | S, O, N, A, T, A, E | W, A, R, S, A, W, E | W, A, G, N, E, R, E | A, U, T, H, O, R, E | M, A, H, L, E, R, E | T, E, C, H, N, O, E | V, I, E, N, N, A, E | F, R, A, N, C, E, E | R, I, D, D, I, M, E | P, O, L, A, N, D, E | V, I, O, L, I, N, E | M, U, N, I, C, H, E | M, E, L, O, D, Y, E | P, R, A, G, U, E, E | W, R, I, T, E, R, E | M, A, Z, U, R, K, A |];
 
 pos_var_strat = first_fail;
 pos_val_strat = indomain_median;