Working on Answers to Exercises appendix

Chapter01/Answers to Exercises/Ex__3_40_digit_subtractor.md

@@ -1,7 +1,7 @@
 __Modern Computer Architecture and Organization Second Edition__, by Jim Ledin. Published by Packt Publishing.
 # Chapter 1, Exercise 3
 
-Modify the program of Exercise 2 to implement subtraction of 40-digit decimal values. Perform borrowing as required. Test with 0-0, 1-0, 1000000-1, and 0-1. What is the result for 0-1?
+Modify the programs of Exercise 1 and Exercise 2 to implement subtraction of 40-digit decimal values. Perform borrowing as required. Test with 0-0, 1-0, 1000000-1, and 0-1. What is the result for 0-1?
 
 # Answer
 See the python file [Ex__3_single_digit_subtractor.py](src/Ex__3_single_digit_subtractor.py) for the single-digit subtractor code.

Chapter01/Answers to Exercises/src/Ex__1_single_digit_adder.py

@@ -14,7 +14,7 @@ def increment_adder(a, b, c):
     if b == 0:       # If accumulator is 0, increment carry
         c = c + 1
 
-    return a, b, c;
+    return a, b, c
 
 # Add two decimal digits passed on the command line.
 # The sum is returned as digit2 and the carry is 0 or 1.
@@ -25,5 +25,4 @@ def add_digits(digit1, digit2):
         [digit1, digit2, carry] = increment_adder(
         digit1, digit2, carry)
 
-    return digit2, carry
-
+    return digit2, carry

Chapter01/Answers to Exercises/src/Ex__2_40_digit_adder.py

@@ -26,7 +26,7 @@ def add_40_digits(str1, str2):
     sum = [0]*max_digits
     carry = [0]*max_digits
     for i in range(max_digits):
-        (sum[i], carry[i]) = Ex__1_single_digit_adder.\
+        (sum[i], carry[i]) = Ex__1_single_digit_adder. \
         add_digits(num1[i], num2[i])
 
     # Ripple the carry values across the digits

Chapter01/Answers to Exercises/src/Ex__2_test_40_digit_adder.py

@@ -6,7 +6,7 @@
 import unittest
 import Ex__2_40_digit_adder
 
-class TestSingleDigitAdder(unittest.TestCase):
+class Test40DigitAdder(unittest.TestCase):
     def test_1(self):
         self.assertEqual(Ex__2_40_digit_adder.add_40_digits(
         "0", "0"), "0")

Chapter01/Answers to Exercises/src/Ex__3_40_digit_subtractor.py

@@ -26,7 +26,7 @@ def subtract_40_digits(str1, str2):
     diff = [0]*max_digits
     carry = [0]*max_digits
     for i in range(max_digits):
-        (diff[i], carry[i]) = Ex__3_single_digit_subtractor.\
+        (diff[i], carry[i]) = Ex__3_single_digit_subtractor. \
         subtract_digits(num1[i], num2[i])
 
     # Ripple the carry values across the digits

Chapter01/Answers to Exercises/src/Ex__3_single_digit_subtractor.py

@@ -15,7 +15,7 @@ def decrement_subtractor(a, b, c):
     if a == 9:       # If accum reached 9, decrement carry
         c = c - 1
 
-    return a, b, c;
+    return a, b, c
 
 # Subtract two decimal digits. The difference is returned as
 # digit1 and the carry output is 0 (borrow) or 1 (not borrow).
@@ -26,5 +26,4 @@ def subtract_digits(digit1, digit2):
         [digit1, digit2, carry] = decrement_subtractor(
         digit1, digit2, carry)
 
-    return digit1, carry
-
+    return digit1, carry

Chapter01/Answers to Exercises/src/Ex__3_test_40_digit_subtractor.py

@@ -6,7 +6,7 @@
 import unittest
 import Ex__3_40_digit_subtractor
 
-class TestSingleDigitSubtractor(unittest.TestCase):
+class Test40DigitSubtractor(unittest.TestCase):
     def test_1(self):
         self.assertEqual(Ex__3_40_digit_subtractor.
         subtract_40_digits("0", "0"), "0")

Chapter06/Answers to Exercises/src/Ex__2_dct_formula.py

@@ -16,14 +16,14 @@
 dct_coef = [[i for i in range(len(x))] for j in range(len(x))]
 for n in range(len(x)):
     for k in range(len(x)):
-        dct_coef[n][k] = math.cos((math.pi/len(x))*(n + 1/2)*k);
+        dct_coef[n][k] = math.cos((math.pi/len(x))*(n + 1/2)*k)
 
 # Compute the DCT
 x_dct = [i for i in range(len(x))]
 for k in range(len(x)):
     x_dct[k] = 0;
     for n in range(len(x)):
-        x_dct[k] += x[n]*dct_coef[n][k];
+        x_dct[k] += x[n]*dct_coef[n][k]
 
 # Print the results
 print('Index', end='')

Chapter07/Answers to Exercises/src/Ex__3_row_column_major_order.py

@@ -29,7 +29,8 @@
 
     total_time = t1 - t0
     col_major_time = col_major_time + total_time
-    print('  Column-major time to fill array: %.2f sec' % total_time)
+    print('  Column-major time to fill array: %.2f sec' %
+          total_time)
 
     t0 = time.time()
     for i in range(dim):
@@ -40,20 +41,25 @@
 
     total_time = t1 - t0
     row_major_time = row_major_time + total_time
-    print('  Row-major time to fill array: %.2f sec' % total_time)
+    print('  Row-major time to fill array: %.2f sec' %
+          total_time)
     print('')
 
 row_major_average = row_major_time / num_passes
 col_major_average = col_major_time / num_passes
 
 if (row_major_average < col_major_average):
     winner = 'row'
-    pct_better = 100 * (col_major_average - row_major_average) / col_major_average
+    pct_better = 100 * (col_major_average -
+        row_major_average) / col_major_average
 else:
     winner = 'column'
-    pct_better = 100 * (row_major_average - col_major_average) / row_major_average
+    pct_better = 100 * (row_major_average -
+        col_major_average) / row_major_average
 
 print('Average row-major time   : %.2f sec' % row_major_average)
 print('Average column-major time: %.2f sec' % col_major_average)
-print('Average time difference  : %.2f sec' % ((row_major_time-col_major_time) / num_passes))
-print(('Winner is ' + winner + '-major indexing; It is faster by %.2f%%') % pct_better)
+print('Average time difference  : %.2f sec' % (
+    (row_major_time-col_major_time) / num_passes))
+print(('Winner is ' + winner +
+    '-major indexing; It is faster by %.2f%%') % pct_better)

Chapter14/Answers to Exercises/Ex__3_install_updates.md

@@ -12,4 +12,4 @@ Update the operating system and other applications and services (such as Java) o
 
 1. If an application has an option to automatically check for updates, ensure it is turned on. You may want to be notified when updates are available but not have them installed automatically.
 
-1. Set up a repeating reminder in your calendar application to notify you to check for updates for all devices and applications at the shortest interval you believe is reasonable, whether it is weekly, biweekly, or monthly. Don't wait too long between updates because your systems are vulnerable during the period between the identification of the vulnerability and your installation of the update that fixes it.
+1. Set up a repeating reminder in your calendar application to notify you to check for updates for all devices and applications at the shortest interval you believe is reasonable, whether it is weekly, biweekly, or monthly. Don't wait too long between updates because your systems are vulnerable during the period between the identification of a vulnerability and your installation of the update that fixes it.

Chapter15/Answers to Exercises/Ex__2_setup_full_bitcoin_node.md

@@ -4,12 +4,12 @@ __Modern Computer Architecture and Organization Second Edition__, by Jim Ledin.
 Set up a full bitcoin peer node and connect it to the bitcoin network. Download the bitcoin core software from https://bitcoin.org/en/download. It is best to have a fast internet connection and at least 200 GB of free disk space.
 
 # Answer
-Download the bitcoin core installer from https://bitcoin.org/en/download and run it.
+1. Download the bitcoin core installer from https://bitcoin.org/en/download and run it.
 
-After installation completes, run the bitcoin core application. The application will begin downloading the entire bitcoin blockchain beginning with the genesis block from 2009 up to the most recently added block. This process will take several hours or days depending on your internet bandwidth.
+1. After installation completes, run the bitcoin core application. The application will begin downloading the entire bitcoin blockchain beginning with the genesis block from 2009 up to the most recently added block. This process will take several hours or days depending on your internet bandwidth.
 
-Although bitcoin core will consume about 200 GB of disk space during the initial validation process, it will reduce its storage requirements to a disk space limit you select, which defaults to 2 GB.
+1. Although bitcoin core will consume about 200 GB of disk space during the initial validation process, it will reduce its storage requirements to a disk space limit you select, which defaults to 2 GB.
 
-After the blockchain has downloaded, the node will transition into operation as a full network peer. You can display the application's connections to network peers and monitor the addition of freshly created transactions into the pool of transactions awaiting inclusion on a future block to be added to the blockchain.
+1. After the blockchain has downloaded, the node will transition into operation as a full network peer. You can display the application's connections to network peers and monitor the addition of freshly created transactions into the pool of transactions awaiting inclusion on a future block to be added to the blockchain.
 
-You can also create a bitcoin wallet within the application and use it to conduct your own bitcoin transactions. If you use this application to store a significant quantity of bitcoin, be certain you are enforcing best security practices for all aspects of the host computer operating system and its applications to ensure you don't get hacked and have your coins stolen.
+1. You can also create a bitcoin wallet within the application and use it to conduct your own bitcoin transactions. If you use this application to store a significant quantity of bitcoin, be certain you are enforcing best security practices for all aspects of the host computer operating system and its applications to ensure you don't get hacked and have your coins stolen.

Chapter15/Answers to Exercises/src/Ex__1_compute_block_hash.py

@@ -9,13 +9,15 @@
 import hashlib    
 
 # The block header copied from bitaps.com
-header = '00000020505424e0dc22a7fb1598d3a048a31957315f737ec0d00b' + \
-    '0000000000000000005f7fbc00ac45edd1f6ca7713f2b048d8a771c95e1' + \
-    'afd9140d3a147a063f64a76781ea461139a0c17f666fc1afdbc08'
+header = '00000020505424e0dc22a7fb1598d3a048a31957315f' + \
+    '737ec0d00b0000000000000000005f7fbc00ac45edd1f6ca7' + \
+    '713f2b048d8a771c95e1afd9140d3a147a063f64a76781ea4' + \
+    '61139a0c17f666fc1afdbc08'
 
 # The hash of the header copied from bitaps.com
 header_hash = \
-    '00000000000000000000bc01913c2e05a5d38d39a9df0c8ba4269abe9777f41f'
+    '00000000000000000000bc01913c2e05a5d38d39a9df0c8ba' + \
+    '4269abe9777f41f'
 
 # Cut off any extra bytes beyond the 80-byte header
 header = header[:160]
@@ -31,9 +33,13 @@
 computed_hash = computed_hash[::-1]
 
 # Convert the binary header hash to a hexadecimal string
-computed_hash = binascii.hexlify(computed_hash).decode("utf-8")
+computed_hash = \
+    binascii.hexlify(computed_hash).decode("utf-8")
 
 # Print the result
+print('Header hash:   ' + header_hash)
+print('Computed hash: ' + computed_hash)
+
 if header_hash == computed_hash:
     result = 'Hashes match!'
 else:

Chapter16/Answers to Exercises/src/Ex__2_load_dataset.py

@@ -6,17 +6,21 @@
 import matplotlib.pyplot as plt
 
 def load_dataset():
-    (train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()
+    (train_images, train_labels), \
+        (test_images, test_labels) = \
+        datasets.cifar10.load_data()
 
     # Normalize pixel values to the range 0-1
     train_images = train_images / 255.0
     test_images = test_images / 255.0
 
-    return train_images, train_labels, test_images, test_labels
+    return train_images, train_labels, \
+        test_images, test_labels
 
 def plot_samples(train_images, train_labels):
-    class_names = ['Airplane', 'Automobile', 'Bird', 'Cat', 'Deer',
-                   'Dog', 'Frog', 'Horse', 'Ship', 'Truck']
+    class_names = ['Airplane', 'Automobile', 'Bird',
+                    'Cat', 'Deer','Dog', 'Frog', 
+                    'Horse', 'Ship', 'Truck']
 
     plt.figure(figsize=(14,7))
     for i in range(60):
@@ -29,5 +33,6 @@ def plot_samples(train_images, train_labels):
     plt.show()
 
 if __name__ == '__main__':
-    train_images, train_labels, test_images, test_labels = load_dataset()
+    train_images, train_labels, \
+        test_images, test_labels = load_dataset()
     plot_samples(train_images, train_labels)

Chapter16/Answers to Exercises/src/Ex__3_create_network.py

@@ -2,16 +2,20 @@
 
 """Ex__3_create_network.py: Answer to Ch 16 Ex 3."""
 
-from tensorflow.keras import datasets, layers, models, optimizers, losses
+from tensorflow.keras import datasets, layers, models, \
+    optimizers, losses
 
 def load_dataset():
-    (train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()
+    (train_images, train_labels), \
+        (test_images, test_labels) = \
+        datasets.cifar10.load_data()
 
     # Normalize pixel values to the range 0-1
     train_images = train_images / 255.0
     test_images = test_images / 255.0
 
-    return train_images, train_labels, test_images, test_labels
+    return train_images, train_labels, \
+        test_images, test_labels
 
 def create_model():
     # Each image is 32x32 pixels with 3 RGB color planes
@@ -34,15 +38,18 @@ def create_model():
 
     model = models.Sequential([
         # First convolutional layer followed by max pooling
-        layers.Conv2D(filters_layer1, conv_filter_size, activation='relu', input_shape=image_shape),
+        layers.Conv2D(filters_layer1, conv_filter_size,
+            activation='relu', input_shape=image_shape),
         layers.MaxPooling2D(pooling_size),
 
         # Second convolutional layer followed by max pooling
-        layers.Conv2D(filters_layer2, conv_filter_size, activation='relu'),
+        layers.Conv2D(filters_layer2, conv_filter_size,
+            activation='relu'),
         layers.MaxPooling2D(pooling_size),
 
         # Third convolutional layer followed by flattening
-        layers.Conv2D(filters_layer3, conv_filter_size, activation='relu'),
+        layers.Conv2D(filters_layer3, conv_filter_size,
+            activation='relu'),
         layers.Flatten(),
 
         # Dense layer followed by the output layer
@@ -51,12 +58,13 @@ def create_model():
     ])
 
     model.compile(optimizer=optimizers.Adam(),
-                  loss=losses.SparseCategoricalCrossentropy(from_logits=True),
-                  metrics=['accuracy'])
+        loss=losses.SparseCategoricalCrossentropy(
+            from_logits=True), metrics=['accuracy'])
 
     return model
 
 if __name__ == '__main__':
-    train_images, train_labels, test_images, test_labels = load_dataset()
+    train_images, train_labels, test_images, \
+        test_labels = load_dataset()
     model = create_model()
     model.summary()

Chapter16/Answers to Exercises/src/Ex__4_train_model.py

@@ -6,13 +6,16 @@
 import matplotlib.pyplot as plt
 
 def load_dataset():
-    (train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()
+    (train_images, train_labels), \
+        (test_images, test_labels) = \
+        datasets.cifar10.load_data()
 
     # Normalize pixel values to the range 0-1
     train_images = train_images / 255.0
     test_images = test_images / 255.0
 
-    return train_images, train_labels, test_images, test_labels
+    return train_images, train_labels, \
+           test_images, test_labels
 
 def create_model():
     # Each image is 32x32 pixels with 3 RGB color planes
@@ -35,15 +38,18 @@ def create_model():
 
     model = models.Sequential([
         # First convolutional layer followed by max pooling
-        layers.Conv2D(filters_layer1, conv_filter_size, activation='relu', input_shape=image_shape),
+        layers.Conv2D(filters_layer1, conv_filter_size,
+            activation='relu', input_shape=image_shape),
         layers.MaxPooling2D(pooling_size),
 
         # Second convolutional layer followed by max pooling
-        layers.Conv2D(filters_layer2, conv_filter_size, activation='relu'),
+        layers.Conv2D(filters_layer2, conv_filter_size,
+            activation='relu'),
         layers.MaxPooling2D(pooling_size),
 
         # Third convolutional layer followed by flattening
-        layers.Conv2D(filters_layer3, conv_filter_size, activation='relu'),
+        layers.Conv2D(filters_layer3, conv_filter_size,
+            activation='relu'),
         layers.Flatten(),
 
         # Dense layer followed by the output layer
@@ -52,23 +58,26 @@ def create_model():
     ])
 
     model.compile(optimizer=optimizers.Adam(),
-                  loss=losses.SparseCategoricalCrossentropy(from_logits=True),
-                  metrics=['accuracy'])
+        loss=losses.SparseCategoricalCrossentropy(
+            from_logits=True), metrics=['accuracy'])
 
     return model
 
-def train_model(train_images, train_labels, test_images, test_labels, model):
-    history = model.fit(train_images, train_labels, epochs=10, 
-                        validation_data=(test_images, test_labels))
+def train_model(train_images, train_labels, \
+                test_images, test_labels, model):
+    history = model.fit(train_images, train_labels,
+        epochs=10, validation_data=(test_images, test_labels))
 
-    test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)
+    test_loss, test_acc = model.evaluate(test_images,
+        test_labels, verbose=2)
 
     return history, test_acc
 
 def plot_model_accuracy(history):
     plt.figure()
     plt.plot(history.history['accuracy'], label='Accuracy')
-    plt.plot(history.history['val_accuracy'], label = 'Validation Accuracy')
+    plt.plot(history.history['val_accuracy'],
+        label = 'Validation Accuracy')
     plt.xlabel('Epoch')
     plt.ylabel('Accuracy')
     plt.ylim([0.5, 1])
@@ -77,11 +86,14 @@ def plot_model_accuracy(history):
     plt.show()
 
 if __name__ == '__main__':
-    train_images, train_labels, test_images, test_labels = load_dataset()
+    train_images, train_labels, test_images, \
+        test_labels = load_dataset()
     model = create_model()
-    history, test_acc = train_model(train_images, train_labels, test_images, test_labels, model)
+    history, test_acc = train_model(train_images, \
+        train_labels, test_images, test_labels, model)
     print()
     print('='*31)
-    print('| Validation accuracy: {:.2f}% |'.format(100*test_acc))
+    print('| Validation accuracy: {:.2f}% |'.
+          format(100*test_acc))
     print('='*31)
     plot_model_accuracy(history)