Fix some more numpy tests

Author: cclauss

Diff for: machine_learning/loss_functions.py

@@ -22,7 +22,7 @@ def binary_cross_entropy(
 
     >>> true_labels = np.array([0, 1, 1, 0, 1])
     >>> predicted_probs = np.array([0.2, 0.7, 0.9, 0.3, 0.8])
-    >>> binary_cross_entropy(true_labels, predicted_probs)
+    >>> float(binary_cross_entropy(true_labels, predicted_probs))
     0.2529995012327421
     >>> true_labels = np.array([0, 1, 1, 0, 1])
     >>> predicted_probs = np.array([0.3, 0.8, 0.9, 0.2])
@@ -420,7 +420,7 @@ def mean_squared_logarithmic_error(y_true: np.ndarray, y_pred: np.ndarray) -> fl
 
     >>> true_values = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
     >>> predicted_values = np.array([0.8, 2.1, 2.9, 4.2, 5.2])
-    >>> mean_squared_logarithmic_error(true_values, predicted_values)
+    >>> float(mean_squared_logarithmic_error(true_values, predicted_values))
     0.0030860877925181344
     >>> true_labels = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
     >>> predicted_probs = np.array([0.3, 0.8, 0.9, 0.2])
@@ -459,17 +459,17 @@ def mean_absolute_percentage_error(
     Examples:
     >>> y_true = np.array([10, 20, 30, 40])
     >>> y_pred = np.array([12, 18, 33, 45])
-    >>> mean_absolute_percentage_error(y_true, y_pred)
+    >>> float(mean_absolute_percentage_error(y_true, y_pred))
     0.13125
 
     >>> y_true = np.array([1, 2, 3, 4])
     >>> y_pred = np.array([2, 3, 4, 5])
-    >>> mean_absolute_percentage_error(y_true, y_pred)
+    >>> float(mean_absolute_percentage_error(y_true, y_pred))
     0.5208333333333333
 
     >>> y_true = np.array([34, 37, 44, 47, 48, 48, 46, 43, 32, 27, 26, 24])
     >>> y_pred = np.array([37, 40, 46, 44, 46, 50, 45, 44, 34, 30, 22, 23])
-    >>> mean_absolute_percentage_error(y_true, y_pred)
+    >>> float(mean_absolute_percentage_error(y_true, y_pred))
     0.064671076436071
     """
     if len(y_true) != len(y_pred):
@@ -511,7 +511,7 @@ def perplexity_loss(
     ...      [[0.03, 0.26, 0.21, 0.18, 0.30],
     ...       [0.28, 0.10, 0.33, 0.15, 0.12]]]
     ... )
-    >>> perplexity_loss(y_true, y_pred)
+    >>> float(perplexity_loss(y_true, y_pred))
     5.0247347775367945
     >>> y_true = np.array([[1, 4], [2, 3]])
     >>> y_pred = np.array(

Diff for: machine_learning/mfcc.py

@@ -162,9 +162,9 @@ def normalize(audio: np.ndarray) -> np.ndarray:
     Examples:
     >>> audio = np.array([1, 2, 3, 4, 5])
     >>> normalized_audio = normalize(audio)
-    >>> np.max(normalized_audio)
+    >>> float(np.max(normalized_audio))
     1.0
-    >>> np.min(normalized_audio)
+    >>> float(np.min(normalized_audio))
     0.2
     """
     # Divide the entire audio signal by the maximum absolute value
@@ -229,7 +229,8 @@ def calculate_fft(audio_windowed: np.ndarray, ftt_size: int = 1024) -> np.ndarra
     Examples:
     >>> audio_windowed = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
     >>> audio_fft = calculate_fft(audio_windowed, ftt_size=4)
-    >>> np.allclose(audio_fft[0], np.array([6.0+0.j, -1.5+0.8660254j, -1.5-0.8660254j]))
+    >>> bool(np.allclose(audio_fft[0], np.array([6.0+0.j, -1.5+0.8660254j,
+    ...     -1.5-0.8660254j])))
     True
     """
     # Transpose the audio data to have time in rows and channels in columns
@@ -281,7 +282,7 @@ def freq_to_mel(freq: float) -> float:
         The frequency in mel scale.
 
     Examples:
-    >>> round(freq_to_mel(1000), 2)
+    >>> float(round(freq_to_mel(1000), 2))
     999.99
     """
     # Use the formula to convert frequency to the mel scale
@@ -321,7 +322,7 @@ def mel_spaced_filterbank(
         Mel-spaced filter bank.
 
     Examples:
-    >>> round(mel_spaced_filterbank(8000, 10, 1024)[0][1], 10)
+    >>> float(round(mel_spaced_filterbank(8000, 10, 1024)[0][1], 10))
     0.0004603981
     """
     freq_min = 0
@@ -438,7 +439,7 @@ def discrete_cosine_transform(dct_filter_num: int, filter_num: int) -> np.ndarra
         The DCT basis matrix.
 
     Examples:
-    >>> round(discrete_cosine_transform(3, 5)[0][0], 5)
+    >>> float(round(discrete_cosine_transform(3, 5)[0][0], 5))
     0.44721
     """
     basis = np.empty((dct_filter_num, filter_num))

Diff for: machine_learning/multilayer_perceptron_classifier.py

@@ -17,7 +17,7 @@
 
 def wrapper(y):
     """
-    >>> wrapper(Y)
+    >>> [int(x) for x in wrapper(Y)]
     [0, 0, 1]
     """
     return list(y)