Fix some more numpy tests

Author: cclauss

data_structures/heap/binomial_heap.py

@@ -118,7 +118,7 @@ class BinomialHeap:
     values in merged heap; (merge is inplace)
     >>> results = []
     >>> while not first_heap.is_empty():
-    ...     results.append(first_heap.delete_min())
+    ...     results.append(int(first_heap.delete_min()))
     >>> results
     [17, 20, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 34]
     """

electronics/circular_convolution.py

@@ -39,7 +39,7 @@ def circular_convolution(self) -> list[float]:
         Usage:
         >>> convolution = CircularConvolution()
         >>> convolution.circular_convolution()
-        [10.0 10.0, 6.0, 14.0]
+        [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]

linear_programming/simplex.py

@@ -108,7 +108,7 @@ def generate_col_titles(self) -> list[str]:
     def find_pivot(self) -> tuple[Any, Any]:
         """Finds the pivot row and column.
         >>> 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()
+        ... [1,2,0,1,7.]]), 2, 0).find_pivot())
         (1, 0)
         """
         objective = self.objectives[-1]

machine_learning/forecasting/run.py

@@ -28,7 +28,7 @@ def linear_regression_prediction(
     input : training data (date, total_user, total_event) in list of float
     output : list of total user prediction in float
     >>> n = linear_regression_prediction([2,3,4,5], [5,3,4,6], [3,1,2,4], [2,1], [2,2])
-    >>> abs(n - 5.0) < 1e-6  # Checking precision because of floating point errors
+    >>> bool(abs(n - 5.0) < 1e-6)  # Checking precision because of floating point errors
     True
     """
     x = np.array([[1, item, train_mtch[i]] for i, item in enumerate(train_dt)])
@@ -56,7 +56,7 @@ def sarimax_predictor(train_user: list, train_match: list, test_match: list) ->
     )
     model_fit = model.fit(disp=False, maxiter=600, method="nm")
     result = model_fit.predict(1, len(test_match), exog=[test_match])
-    return result[0]
+    return float(result[0])
 
 
 def support_vector_regressor(x_train: list, x_test: list, train_user: list) -> float:
@@ -75,7 +75,7 @@ def support_vector_regressor(x_train: list, x_test: list, train_user: list) -> f
     regressor = SVR(kernel="rbf", C=1, gamma=0.1, epsilon=0.1)
     regressor.fit(x_train, train_user)
     y_pred = regressor.predict(x_test)
-    return y_pred[0]
+    return float(y_pred[0])
 
 
 def interquartile_range_checker(train_user: list) -> float:
@@ -92,7 +92,7 @@ def interquartile_range_checker(train_user: list) -> float:
     q3 = np.percentile(train_user, 75)
     iqr = q3 - q1
     low_lim = q1 - (iqr * 0.1)
-    return low_lim
+    return float(low_lim)
 
 
 def data_safety_checker(list_vote: list, actual_result: float) -> bool: