Fix some more numpy tests

Author: cclauss

data_structures/heap/binomial_heap.py

@@ -73,7 +73,7 @@ class BinomialHeap:
     30
 
     Deleting - delete() test
-    >>> [first_heap.delete_min() for _ in range(20)]
+    >>> [int(first_heap.delete_min()) for _ in range(20)]
     [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]
 
     Create a new Heap

electronics/circular_convolution.py

@@ -39,7 +39,7 @@ def circular_convolution(self) -> list[float]:
         Usage:
         >>> convolution = CircularConvolution()
         >>> convolution.circular_convolution()
-        [10, 10, 6, 14]
+        [10.0 10.0, 6.0, 14.0]
 
         >>> convolution.first_signal = [0.2, 0.4, 0.6, 0.8, 1.0, 1.2, 1.4, 1.6]
         >>> convolution.second_signal = [0.1, 0.3, 0.5, 0.7, 0.9, 1.1, 1.3, 1.5]

fractals/julia_sets.py

@@ -42,9 +42,9 @@ def eval_exponential(c_parameter: complex, z_values: np.ndarray) -> np.ndarray:
     Evaluate $e^z + c$.
     >>> float(eval_exponential(0, 0))
     1.0
-    >>> abs(eval_exponential(1, np.pi*1.j)) < 1e-15
+    >>> bool(abs(eval_exponential(1, np.pi*1.j)) < 1e-15)
     True
-    >>> abs(eval_exponential(1.j, 0)-1-1.j) < 1e-15
+    >>> bool(abs(eval_exponential(1.j, 0)-1-1.j) < 1e-15)
     True
     """
     return np.exp(z_values) + c_parameter

graphics/bezier_curve.py

@@ -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)])
-        >>> [float(x) for x in curve.bezier_curve_function(0)]
+        >>> tuple(float(x) for x in curve.bezier_curve_function(0))
         (1.0, 1.0)
-        >>> [float(x) for x in curve.bezier_curve_function(1)]
+        >>> tuple(float(x) for x in curve.bezier_curve_function(1))
         (1.0, 2.0)
         """
 

neural_network/two_hidden_layers_neural_network.py

@@ -105,7 +105,7 @@ def back_propagation(self) -> None:
         >>> res = nn.feedforward()
         >>> nn.back_propagation()
         >>> updated_weights = nn.second_hidden_layer_and_output_layer_weights
-        >>> (res == updated_weights).all()
+        >>> bool((res == updated_weights).all())
         False
         """