Question

Please refer to the existing functions. Don't change them. Solution has to include the functions that are already created.

def hinge_loss_single(feature_vector, label, theta, theta_0):
"""
Finds the hinge loss on a single data point given specific classification
parameters.

Args:
feature_vector - A numpy array describing the given data point.
label - A real valued number, the correct classification of the data
point.
theta - A numpy array describing the linear classifier.
theta_0 - A real valued number representing the offset parameter.

Returns: A real number representing the hinge loss associated with the
given data point and parameters.
"""
# Your code below

# your code above
raise NotImplementedError

def hinge_loss_full(feature_matrix, labels, theta, theta_0):
"""
Finds the total hinge loss on a set of data given specific classification
parameters.

Args:
feature_matrix - A numpy matrix describing the given data. Each row
represents a single data point.
labels - A numpy array where the kth element of the array is the
correct classification of the kth row of the feature matrix.
theta - A numpy array describing the linear classifier.
theta_0 - A real valued number representing the offset parameter.

Returns: A real number representing the hinge loss associated with the
given dataset and parameters. This number should be the average hinge
loss across all of the points in the feature matrix.
"""
# Your code below

# your code above
raise NotImplementedError
def hinge_loss_single(feature_vector, label, theta, theta_): Finds the hinge Loss on a single data point given specific classification parameters. Args: feature_vector - A numpy array describing the given data point. Label - A real valued number, the correct classification of the data point. theta - A numpy array describing the linear classifier. theta_0 - A real valued number representing the offset parameter. Returns: A real number representing the hinge Loss associated with the given data point and parameters. AF BF BF # Your code below # your code above raise NotImplementedError

Answer 1

Working code implemented in Python and appropriate comments provided for better understanding:

Source code for main.py:

def hinge_loss_single(feature_vector, label, theta, theta_0):
# Finds the hinge loss on a single data point given specific classification parameters.
if label * (np.dot(feature_vector, theta) + theta_0) > 0:
return 0
else:
return 1 - label * (np.dot(feature_vector, theta) + theta_0)

def hinge_loss_full(feature_matrix, labels, theta, theta_0):
# Finds the total hinge loss on a set of data given specific classification parameters.
t = np.arange(1000)
sgn = np.arange(1000)
sum = 0
for i in range(len(feature_matrix)):
sum = sum + hinge_loss_single(feature_matrix[i], labels[i], theta, theta_0)
return sum / len(feature_matrix)

Code Screenshots:

def hinge_loss_single(feature_vector, Label, theta, theta_®): # Finds the hinge loss on a single data point given specific classification parameters. if label * (np.dot(feature_vector, theta) + theta_e) > 0: return 0 else: return 1 label (np.dot(feature_vector, theta) + theta_e) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 def hinge_loss_full(feature_matrix, Labels, theta, theta_0): # Finds the total hinge loss on a set of data given specific classification parameters. t = np.arange (1000) sgn np.arange (1000) sum = 0 for i in range(len(feature_matrix)): sum = sum + hinge_loss_single(feature_matrix[i], labels[i], theta, theta_e) return sum / len(feature_matrix)

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