avendesora · sourcery-ai · Mar 21, 2022 · sourcery-ai · Mar 21, 2022 · sourcery-ai
diff --git a/Chapter1/fib2.py b/Chapter1/fib2.py
@@ -16,9 +16,7 @@
 
 
 def fib2(n: int) -> int:
-    if n < 2:  # base case
-        return n
-    return fib2(n - 2) + fib2(n - 1)  # recursive case
+    return n if n < 2 else fib2(n - 2) + fib2(n - 1)
 
 
 if __name__ == "__main__":

diff --git a/Chapter1/fib4.py b/Chapter1/fib4.py
@@ -18,9 +18,7 @@
 
 @lru_cache(maxsize=None)
 def fib4(n: int) -> int:  # same definition as fib2()
-    if n < 2:  # base case
-        return n
-    return fib4(n - 2) + fib4(n - 1)  # recursive case
+    return n if n < 2 else fib4(n - 2) + fib4(n - 1)
 
 
 if __name__ == "__main__":

diff --git a/Chapter1/trivial_compression.py b/Chapter1/trivial_compression.py
@@ -32,22 +32,22 @@ def _compress(self, gene: str) -> None:
             elif nucleotide == "T":  # change last two bits to 11
                 self.bit_string |= 0b11
             else:
-                raise ValueError("Invalid Nucleotide:{}".format(nucleotide))
+                raise ValueError(f"Invalid Nucleotide:{nucleotide}")
 
     def decompress(self) -> str:
         gene: str = ""
         for i in range(0, self.bit_string.bit_length() - 1, 2):  # - 1 to exclude sentinel
             bits: int = self.bit_string >> i & 0b11  # get just 2 relevant bits
-            if bits == 0b00:  # A
+            if bits == 0b00:
                 gene += "A"
-            elif bits == 0b01:  # C
+            elif bits == 0b01:
                 gene += "C"
-            elif bits == 0b10:  # G
+            elif bits == 0b10:
                 gene += "G"
-            elif bits == 0b11:  # T
+            elif bits == 0b11:
                 gene += "T"
             else:
-                raise ValueError("Invalid bits:{}".format(bits))
+                raise ValueError(f"Invalid bits:{bits}")
         return gene[::-1]  # [::-1] reverses string by slicing backwards
 
     def __str__(self) -> str:  # string representation for pretty printing
@@ -57,8 +57,10 @@ def __str__(self) -> str:  # string representation for pretty printing
 if __name__ == "__main__":
     from sys import getsizeof
     original: str = "TAGGGATTAACCGTTATATATATATAGCCATGGATCGATTATATAGGGATTAACCGTTATATATATATAGCCATGGATCGATTATA" * 100
-    print("original is {} bytes".format(getsizeof(original)))
+    print(f"original is {getsizeof(original)} bytes")
     compressed: CompressedGene = CompressedGene(original)  # compress
-    print("compressed is {} bytes".format(getsizeof(compressed.bit_string)))
+    print(f"compressed is {getsizeof(compressed.bit_string)} bytes")
     print(compressed)  # decompress
-    print("original and decompressed are the same: {}".format(original == compressed.decompress()))
+    print(
+        f"original and decompressed are the same: {original == compressed.decompress()}"
+    )
diff --git a/Chapter2/dna_search.py b/Chapter2/dna_search.py
@@ -38,10 +38,7 @@ def string_to_gene(s: str) -> Gene:
 
 
 def linear_contains(gene: Gene, key_codon: Codon) -> bool:
-    for codon in gene:
-        if codon == key_codon:
-            return True
-    return False
+    return key_codon in gene
 
 
 acg: Codon = (Nucleotide.A, Nucleotide.C, Nucleotide.G)

diff --git a/Chapter2/generic_search.py b/Chapter2/generic_search.py
@@ -22,10 +22,7 @@
 
 
 def linear_contains(iterable: Iterable[T], key: T) -> bool:
-    for item in iterable:
-        if item == key:
-            return True
-    return False
+    return key in iterable
 
 
 C = TypeVar("C", bound="Comparable")

diff --git a/Chapter2/maze.py b/Chapter2/maze.py
@@ -41,7 +41,10 @@ def __init__(self, rows: int = 10, columns: int = 10, sparseness: float = 0.2, s
         self.start: MazeLocation = start
         self.goal: MazeLocation = goal
         # fill the grid with empty cells
-        self._grid: List[List[Cell]] = [[Cell.EMPTY for c in range(columns)] for r in range(rows)]
+        self._grid: List[List[Cell]] = [
+            [Cell.EMPTY for _ in range(columns)] for _ in range(rows)
+        ]
+
         # populate the grid with blocked cells
         self._randomly_fill(rows, columns, sparseness)
         # fill the start and goal locations in
@@ -56,10 +59,7 @@ def _randomly_fill(self, rows: int, columns: int, sparseness: float):
 
     # return a nicely formatted version of the maze for printing
     def __str__(self) -> str:
-        output: str = ""
-        for row in self._grid:
-            output += "".join([c.value for c in row]) + "\n"
-        return output
+        return "".join("".join([c.value for c in row]) + "\n" for row in self._grid)
 
     def goal_test(self, ml: MazeLocation) -> bool:
         return ml == self.goal
@@ -68,11 +68,11 @@ def successors(self, ml: MazeLocation) -> List[MazeLocation]:
         locations: List[MazeLocation] = []
         if ml.row + 1 < self._rows and self._grid[ml.row + 1][ml.column] != Cell.BLOCKED:
             locations.append(MazeLocation(ml.row + 1, ml.column))
-        if ml.row - 1 >= 0 and self._grid[ml.row - 1][ml.column] != Cell.BLOCKED:
+        if ml.row >= 1 and self._grid[ml.row - 1][ml.column] != Cell.BLOCKED:
             locations.append(MazeLocation(ml.row - 1, ml.column))
         if ml.column + 1 < self._columns and self._grid[ml.row][ml.column + 1] != Cell.BLOCKED:
             locations.append(MazeLocation(ml.row, ml.column + 1))
-        if ml.column - 1 >= 0 and self._grid[ml.row][ml.column - 1] != Cell.BLOCKED:
+        if ml.column >= 1 and self._grid[ml.row][ml.column - 1] != Cell.BLOCKED:
             locations.append(MazeLocation(ml.row, ml.column - 1))
         return locations
 
@@ -93,7 +93,7 @@ def euclidean_distance(goal: MazeLocation) -> Callable[[MazeLocation], float]:
     def distance(ml: MazeLocation) -> float:
         xdist: int = ml.column - goal.column
         ydist: int = ml.row - goal.row
-        return sqrt((xdist * xdist) + (ydist * ydist))
+        return sqrt(xdist**2 + ydist**2)
     return distance
 
 

diff --git a/Chapter2/missionaries.py b/Chapter2/missionaries.py
@@ -73,7 +73,7 @@ def successors(self) -> List[MCState]:
 
 
 def display_solution(path: List[MCState]):
-    if len(path) == 0: # sanity check
+    if not path: # sanity check
         return
     old_state: MCState = path[0]
     print(old_state)

diff --git a/Chapter3/csp.py b/Chapter3/csp.py
@@ -55,10 +55,10 @@ def add_constraint(self, constraint: Constraint[V, D]) -> None:
     # Check if the value assignment is consistent by checking all constraints
     # for the given variable against it
     def consistent(self, variable: V, assignment: Dict[V, D]) -> bool:
-        for constraint in self.constraints[variable]:
-            if not constraint.satisfied(assignment):
-                return False
-        return True
+        return all(
+            constraint.satisfied(assignment)
+            for constraint in self.constraints[variable]
+        )
 
     def backtracking_search(self, assignment: Dict[V, D] = {}) -> Optional[Dict[V, D]]:
         # assignment is complete if every variable is assigned (our base case)

diff --git a/Chapter3/map_coloring.py b/Chapter3/map_coloring.py
@@ -36,9 +36,10 @@ def satisfied(self, assignment: Dict[str, str]) -> bool:
 if __name__ == "__main__":
     variables: List[str] = ["Western Australia", "Northern Territory", "South Australia",
                             "Queensland", "New South Wales", "Victoria", "Tasmania"]
-    domains: Dict[str, List[str]] = {}
-    for variable in variables:
-        domains[variable] = ["red", "green", "blue"]
+    domains: Dict[str, List[str]] = {
+        variable: ["red", "green", "blue"] for variable in variables
+    }
+
     csp: CSP[str, str] = CSP(variables, domains)
     csp.add_constraint(MapColoringConstraint("Western Australia", "Northern Territory"))
     csp.add_constraint(MapColoringConstraint("Western Australia", "South Australia"))

diff --git a/Chapter3/queens.py b/Chapter3/queens.py
@@ -38,9 +38,10 @@ def satisfied(self, assignment: Dict[int, int]) -> bool:
 
 if __name__ == "__main__":
     columns: List[int] = [1, 2, 3, 4, 5, 6, 7, 8]
-    rows: Dict[int, List[int]] = {}
-    for column in columns:
-        rows[column] = [1, 2, 3, 4, 5, 6, 7, 8]
+    rows: Dict[int, List[int]] = {
+        column: [1, 2, 3, 4, 5, 6, 7, 8] for column in columns
+    }
+
     csp: CSP[int, int] = CSP(columns, rows)
     csp.add_constraint(QueensConstraint(columns))
     solution: Optional[Dict[int, int]] = csp.backtracking_search()

diff --git a/Chapter3/send_more_money.py b/Chapter3/send_more_money.py
@@ -46,9 +46,10 @@ def satisfied(self, assignment: Dict[str, int]) -> bool:
 
 if __name__ == "__main__":
     letters: List[str] = ["S", "E", "N", "D", "M", "O", "R", "Y"]
-    possible_digits: Dict[str, List[int]] = {}
-    for letter in letters:
-        possible_digits[letter] = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
+    possible_digits: Dict[str, List[int]] = {
+        letter: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] for letter in letters
+    }
+
     possible_digits["M"] = [1]  # so we don't get answers starting with a 0
     csp: CSP[str, int] = CSP(letters, possible_digits)
     csp.add_constraint(SendMoreMoneyConstraint(letters))

diff --git a/Chapter3/word_search.py b/Chapter3/word_search.py
@@ -28,7 +28,7 @@ class GridLocation(NamedTuple):
 
 def generate_grid(rows: int, columns: int) -> Grid:
     # initialize grid with random letters
-    return [[choice(ascii_uppercase) for c in range(columns)] for r in range(rows)]
+    return [[choice(ascii_uppercase) for _ in range(columns)] for _ in range(rows)]
 
 
 def display_grid(grid: Grid) -> None:
@@ -74,9 +74,10 @@ def satisfied(self, assignment: Dict[str, List[GridLocation]]) -> bool:
 if __name__ == "__main__":
     grid: Grid = generate_grid(9, 9)
     words: List[str] = ["MATTHEW", "JOE", "MARY", "SARAH", "SALLY"]
-    locations: Dict[str, List[List[GridLocation]]] = {}
-    for word in words:
-        locations[word] = generate_domain(word, grid)
+    locations: Dict[str, List[List[GridLocation]]] = {
+        word: generate_domain(word, grid) for word in words
+    }
+
     csp: CSP[str, List[GridLocation]] = CSP(words, locations)
     csp.add_constraint(WordSearchConstraint(words))
     solution: Optional[Dict[str, List[GridLocation]]] = csp.backtracking_search()

diff --git a/Chapter4/dijkstra.py b/Chapter4/dijkstra.py
@@ -66,20 +66,16 @@ def dijkstra(wg: WeightedGraph[V], root: V) -> Tuple[List[Optional[float]], Dict
 
 # Helper function to get easier access to dijkstra results
 def distance_array_to_vertex_dict(wg: WeightedGraph[V], distances: List[Optional[float]]) -> Dict[V, Optional[float]]:
-    distance_dict: Dict[V, Optional[float]] = {}
-    for i in range(len(distances)):
-        distance_dict[wg.vertex_at(i)] = distances[i]
-    return distance_dict
+    return {wg.vertex_at(i): distances[i] for i in range(len(distances))}
 
 
 # Takes a dictionary of edges to reach each node and returns a list of
 # edges that goes from `start` to `end`
 def path_dict_to_path(start: int, end: int, path_dict: Dict[int, WeightedEdge]) -> WeightedPath:
-    if len(path_dict) == 0:
+    if not path_dict:
         return []
-    edge_path: WeightedPath = []
     e: WeightedEdge = path_dict[end]
-    edge_path.append(e)
+    edge_path: WeightedPath = [e]
     while e.u != start:
         e = path_dict[e.u]
         edge_path.append(e)

diff --git a/Chapter4/graph.py b/Chapter4/graph.py
@@ -82,10 +82,10 @@ def edges_for_vertex(self, vertex: V) -> List[Edge]:
 
     # Make it easy to pretty-print a Graph
     def __str__(self) -> str:
-        desc: str = ""
-        for i in range(self.vertex_count):
-            desc += f"{self.vertex_at(i)} -> {self.neighbors_for_index(i)}\n"
-        return desc
+        return "".join(
+            f"{self.vertex_at(i)} -> {self.neighbors_for_index(i)}\n"
+            for i in range(self.vertex_count)
+        )
 
 
 if __name__ == "__main__":

diff --git a/Chapter4/mst.py b/Chapter4/mst.py
@@ -23,7 +23,7 @@
 
 
 def total_weight(wp: WeightedPath) -> float:
-    return sum([e.weight for e in wp])
+    return sum(e.weight for e in wp)
 
 
 def mst(wg: WeightedGraph[V], start: int = 0) -> Optional[WeightedPath]:

diff --git a/Chapter4/weighted_graph.py b/Chapter4/weighted_graph.py
@@ -35,16 +35,16 @@ def add_edge_by_vertices(self, first: V, second: V, weight: float) -> None:
         self.add_edge_by_indices(u, v, weight)
 
     def neighbors_for_index_with_weights(self, index: int) -> List[Tuple[V, float]]:
-        distance_tuples: List[Tuple[V, float]] = []
-        for edge in self.edges_for_index(index):
-            distance_tuples.append((self.vertex_at(edge.v), edge.weight))
-        return distance_tuples
+        return [
+            (self.vertex_at(edge.v), edge.weight)
+            for edge in self.edges_for_index(index)
+        ]
 
     def __str__(self) -> str:
-        desc: str = ""
-        for i in range(self.vertex_count):
-            desc += f"{self.vertex_at(i)} -> {self.neighbors_for_index_with_weights(i)}\n"
-        return desc
+        return "".join(
+            f"{self.vertex_at(i)} -> {self.neighbors_for_index_with_weights(i)}\n"
+            for i in range(self.vertex_count)
+        )
 
 
 if __name__ == "__main__":

diff --git a/Chapter5/simple_equation.py b/Chapter5/simple_equation.py
@@ -46,11 +46,10 @@ def mutate(self) -> None:
                 self.x += 1
             else:
                 self.x -= 1
-        else: # otherwise mutate y
-            if random() > 0.5:
-                self.y += 1
-            else:
-                self.y -= 1
+        elif random() > 0.5:
+            self.y += 1
+        else:
+            self.y -= 1
 
     def __str__(self) -> str:
         return f"X: {self.x} Y: {self.y} Fitness: {self.fitness()}"

diff --git a/Chapter6/data_point.py b/Chapter6/data_point.py
@@ -33,9 +33,11 @@ def distance(self, other: DataPoint) -> float:
         return sqrt(sum(differences))
 
     def __eq__(self, other: object) -> bool:
-        if not isinstance(other, DataPoint):
-            return NotImplemented
-        return self.dimensions == other.dimensions
+        return (
+            self.dimensions == other.dimensions
+            if isinstance(other, DataPoint)
+            else NotImplemented
+        )
 
     def __repr__(self) -> str:
         return self._originals.__repr__()
diff --git a/Chapter7/layer.py b/Chapter7/layer.py
@@ -25,11 +25,13 @@ def __init__(self, previous_layer: Optional[Layer], num_neurons: int, learning_r
         self.previous_layer: Optional[Layer] = previous_layer
         self.neurons: List[Neuron] = []
         # the following could all be one large list comprehension, but gets a bit long that way
-        for i in range(num_neurons):
-            if previous_layer is None:
-                random_weights: List[float] = []
-            else:
-                random_weights = [random() for _ in range(len(previous_layer.neurons))]
+        for _ in range(num_neurons):
+            random_weights = (
+                []
+                if previous_layer is None
+                else [random() for _ in range(len(previous_layer.neurons))]
+            )
+
             neuron: Neuron = Neuron(random_weights, learning_rate, activation_function, derivative_activation_function)
             self.neurons.append(neuron)
         self.output_cache: List[float] = [0.0 for _ in range(num_neurons)]

diff --git a/Chapter7/util.py b/Chapter7/util.py
@@ -39,6 +39,6 @@ def normalize_by_feature_scaling(dataset: List[List[float]]) -> None:
         column: List[float] = [row[col_num] for row in dataset]
         maximum = max(column)
         minimum = min(column)
-        for row_num in range(len(dataset)):
-            dataset[row_num][col_num] = (dataset[row_num][col_num] - minimum) / (maximum - minimum)
+        for item in dataset:
+            item[col_num] = (item[col_num] - minimum) / (maximum - minimum)