Fix some more numpy tests

Author: cclauss

data_structures/heap/binomial_heap.py

@@ -354,7 +354,7 @@ def delete_min(self):
         # Merge heaps
         self.merge_heaps(new_heap)
 
-        return min_value
+        return int(min_value)
 
     def pre_order(self):
         """

electronics/circular_convolution.py

@@ -91,7 +91,7 @@ def circular_convolution(self) -> list[float]:
         final_signal = np.matmul(np.transpose(matrix), np.transpose(self.first_signal))
 
         # rounding-off to two decimal places
-        return [round(i, 2) for i in final_signal]
+        return [float(round(i, 2)) for i in final_signal]
 
 
 if __name__ == "__main__":

fractals/julia_sets.py

@@ -40,7 +40,7 @@
 def eval_exponential(c_parameter: complex, z_values: np.ndarray) -> np.ndarray:
     """
     Evaluate $e^z + c$.
-    >>> eval_exponential(0, 0)
+    >>> float(eval_exponential(0, 0))
     1.0
     >>> abs(eval_exponential(1, np.pi*1.j)) < 1e-15
     True
@@ -101,17 +101,17 @@ def iterate_function(
     >>> np.round(iterate_function(eval_quadratic_polynomial,
     ... 0,
     ... 3,
-    ... np.array([0,1,2]))[0])
+    ... complex(np.array([0,1,2]))[0]))
     0j
     >>> np.round(iterate_function(eval_quadratic_polynomial,
     ... 0,
     ... 3,
-    ... np.array([0,1,2]))[1])
+    ... complex(np.array([0,1,2]))[1]))
     (1+0j)
     >>> np.round(iterate_function(eval_quadratic_polynomial,
     ... 0,
     ... 3,
-    ... np.array([0,1,2]))[2])
+    ... complex(np.array([0,1,2]))[2]))
     (256+0j)
     """
 

graphics/bezier_curve.py

@@ -30,9 +30,9 @@ def basis_function(self, t: float) -> list[float]:
         returns the x, y values of basis function at time t
 
         >>> curve = BezierCurve([(1,1), (1,2)])
-        >>> curve.basis_function(0)
+        >>> [float(x) for x in curve.basis_function(0)]
         [1.0, 0.0]
-        >>> curve.basis_function(1)
+        >>> [float(x) for x in curve.basis_function(1)]
         [0.0, 1.0]
         """
         assert 0 <= t <= 1, "Time t must be between 0 and 1."
@@ -55,9 +55,9 @@ def bezier_curve_function(self, t: float) -> tuple[float, float]:
             The last point in the curve is when t = 1.
 
         >>> curve = BezierCurve([(1,1), (1,2)])
-        >>> curve.bezier_curve_function(0)
+        >>> [float(x) for x in curve.bezier_curve_function(0)]
         (1.0, 1.0)
-        >>> curve.bezier_curve_function(1)
+        >>> [float(x) for x in curve.bezier_curve_function(1)]
         (1.0, 2.0)
         """
 

graphs/dijkstra_binary_grid.py

@@ -69,7 +69,7 @@ def dijkstra(
                 x, y = predecessors[x, y]
             path.append(source)  # add the source manually
             path.reverse()
-            return matrix[destination], path
+            return float(matrix[destination]), path
 
         for i in range(len(dx)):
             nx, ny = x + dx[i], y + dy[i]

linear_algebra/src/power_iteration.py

@@ -78,7 +78,7 @@ def power_iteration(
     if is_complex:
         lambda_ = np.real(lambda_)
 
-    return lambda_, vector
+    return float(lambda_), vector
 
 
 def test_power_iteration() -> None:

linear_programming/simplex.py

@@ -107,8 +107,8 @@ def generate_col_titles(self) -> list[str]:
 
     def find_pivot(self) -> tuple[Any, Any]:
         """Finds the pivot row and column.
-        >>> Tableau(np.array([[-2,1,0,0,0], [3,1,1,0,6], [1,2,0,1,7.]]),
-        ... 2, 0).find_pivot()
+        >>> tuple(int(x) for x in Tableau(np.array([[-2,1,0,0,0], [3,1,1,0,6],
+        ... [1,2,0,1,7.]]), 2, 0).find_pivot()
         (1, 0)
         """
         objective = self.objectives[-1]
@@ -215,56 +215,56 @@ def run_simplex(self) -> dict[Any, Any]:
         Max:  x1 +  x2
         ST:   x1 + 3x2 <= 4
              3x1 +  x2 <= 4
-        >>> Tableau(np.array([[-1,-1,0,0,0],[1,3,1,0,4],[3,1,0,1,4.]]),
-        ... 2, 0).run_simplex()
+        >>> {key: float(value) for key, value in Tableau(np.array([[-1,-1,0,0,0],
+        ... [1,3,1,0,4],[3,1,0,1,4.]]), 2, 0).run_simplex().items()}
         {'P': 2.0, 'x1': 1.0, 'x2': 1.0}
 
         # Standard linear program with 3 variables:
         Max: 3x1 +  x2 + 3x3
         ST:  2x1 +  x2 +  x3 ≤ 2
               x1 + 2x2 + 3x3 ≤ 5
              2x1 + 2x2 +  x3 ≤ 6
-        >>> Tableau(np.array([
+        >>> {key: float(value) for key, value in Tableau(np.array([
         ... [-3,-1,-3,0,0,0,0],
         ... [2,1,1,1,0,0,2],
         ... [1,2,3,0,1,0,5],
         ... [2,2,1,0,0,1,6.]
-        ... ]),3,0).run_simplex() # doctest: +ELLIPSIS
+        ... ]),3,0).run_simplex().items()} # doctest: +ELLIPSIS
         {'P': 5.4, 'x1': 0.199..., 'x3': 1.6}
 
 
         # Optimal tableau input:
-        >>> Tableau(np.array([
+        >>> {key: float(value) for key, value in Tableau(np.array([
         ... [0, 0, 0.25, 0.25, 2],
         ... [0, 1, 0.375, -0.125, 1],
         ... [1, 0, -0.125, 0.375, 1]
-        ... ]), 2, 0).run_simplex()
+        ... ]), 2, 0).run_simplex().items()}
         {'P': 2.0, 'x1': 1.0, 'x2': 1.0}
 
         # Non-standard: >= constraints
         Max: 2x1 + 3x2 +  x3
         ST:   x1 +  x2 +  x3 <= 40
              2x1 +  x2 -  x3 >= 10
                  -  x2 +  x3 >= 10
-        >>> Tableau(np.array([
+        >>> {key: float(value) for key, value in Tableau(np.array([
         ... [2, 0, 0, 0, -1, -1, 0, 0, 20],
         ... [-2, -3, -1, 0, 0, 0, 0, 0, 0],
         ... [1, 1, 1, 1, 0, 0, 0, 0, 40],
         ... [2, 1, -1, 0, -1, 0, 1, 0, 10],
         ... [0, -1, 1, 0, 0, -1, 0, 1, 10.]
-        ... ]), 3, 2).run_simplex()
+        ... ]), 3, 2).run_simplex().items()}
         {'P': 70.0, 'x1': 10.0, 'x2': 10.0, 'x3': 20.0}
 
         # Non standard: minimisation and equalities
         Min: x1 +  x2
         ST: 2x1 +  x2 = 12
             6x1 + 5x2 = 40
-        >>> Tableau(np.array([
+        >>> {key: float(value) for key, value in Tableau(np.array([
         ... [8, 6, 0, 0, 52],
         ... [1, 1, 0, 0, 0],
         ... [2, 1, 1, 0, 12],
         ... [6, 5, 0, 1, 40.],
-        ... ]), 2, 2).run_simplex()
+        ... ]), 2, 2).run_simplex().items()}
         {'P': 7.0, 'x1': 5.0, 'x2': 2.0}
 
 
@@ -275,15 +275,15 @@ def run_simplex(self) -> dict[Any, Any]:
              2x1 + 4x2 <= 48
               x1 +  x2 >= 10
              x1        >= 2
-        >>> Tableau(np.array([
+        >>> {key: float(value) for key, value in Tableau(np.array([
         ... [2, 1, 0, 0, 0, -1, -1, 0, 0, 12.0],
         ... [-8, -6, 0, 0, 0, 0, 0, 0, 0, 0.0],
         ... [1, 3, 1, 0, 0, 0, 0, 0, 0, 33.0],
         ... [4, 2, 0, 1, 0, 0, 0, 0, 0, 60.0],
         ... [2, 4, 0, 0, 1, 0, 0, 0, 0, 48.0],
         ... [1, 1, 0, 0, 0, -1, 0, 1, 0, 10.0],
         ... [1, 0, 0, 0, 0, 0, -1, 0, 1, 2.0]
-        ... ]), 2, 2).run_simplex() # doctest: +ELLIPSIS
+        ... ]), 2, 2).run_simplex().items()} # doctest: +ELLIPSIS
         {'P': 132.0, 'x1': 12.000... 'x2': 5.999...}
         """
         # Stop simplex algorithm from cycling.
@@ -307,11 +307,11 @@ def run_simplex(self) -> dict[Any, Any]:
     def interpret_tableau(self) -> dict[str, float]:
         """Given the final tableau, add the corresponding values of the basic
         decision variables to the `output_dict`
-        >>> Tableau(np.array([
+        >>> {key: float(value) for key, value in Tableau(np.array([
         ... [0,0,0.875,0.375,5],
         ... [0,1,0.375,-0.125,1],
         ... [1,0,-0.125,0.375,1]
-        ... ]),2, 0).interpret_tableau()
+        ... ]),2, 0).interpret_tableau().items()}
         {'P': 5.0, 'x1': 1.0, 'x2': 1.0}
         """
         # P = RHS of final tableau

machine_learning/decision_tree.py

@@ -26,15 +26,15 @@ def mean_squared_error(self, labels, prediction):
         >>> tester = DecisionTree()
         >>> test_labels = np.array([1,2,3,4,5,6,7,8,9,10])
         >>> test_prediction = float(6)
-        >>> tester.mean_squared_error(test_labels, test_prediction) == (
+        >>> bool(tester.mean_squared_error(test_labels, test_prediction) == (
         ...     TestDecisionTree.helper_mean_squared_error_test(test_labels,
-        ...         test_prediction))
+        ...         test_prediction)))
         True
         >>> test_labels = np.array([1,2,3])
         >>> test_prediction = float(2)
-        >>> tester.mean_squared_error(test_labels, test_prediction) == (
+        >>> bool(tester.mean_squared_error(test_labels, test_prediction) == (
         ...     TestDecisionTree.helper_mean_squared_error_test(test_labels,
-        ...         test_prediction))
+        ...         test_prediction)))
         True
         """
         if labels.ndim != 1: