def train(self):
"""
TODO: Question 1 - Binary Perceptron
Train the perceptron until convergence.
To iterate through all of the data points once (a single epoch), you can
do:
for x, y in self.get_data_and_monitor(self):
...
get_data_and_monitor yields data points one at a time. It also takes the
perceptron as an argument so that it can monitor performance and display
graphics in between yielding data points.
"""
# "*** YOUR CODE HERE ***"
train_over = False
cnt = 0
while not train_over:
train_over = True
cnt = 0
for x, y in self.get_data_and_monitor(self):
if self.update(x, y):
cnt += 1
train_over = False
if not train_over:
self.get_data_and_monitor = backend.make_get_data_and_monitor_perceptron(
)
def __init__(self, dimensions):
"""
Initialize a new Perceptron instance.
A perceptron classifies data points as either belonging to a particular
class (+1) or not (-1). `dimensions` is the dimensionality of the data.
For example, dimensions=2 would mean that the perceptron must classify
2D points.
"""
self.get_data_and_monitor = backend.make_get_data_and_monitor_perceptron()
self.weights = np.zeros(dimensions)
def __init__(self, dimensions):
"""
Initialize a new Perceptron instance.
A perceptron classifies data points as either belonging to a particular
class (+1) or not (-1). `dimensions` is the dimensionality of the data.
For example, dimensions=2 would mean that the perceptron must classify
2D points.
"""
self.get_data_and_monitor = backend.make_get_data_and_monitor_perceptron(
)
#create and store a weight vector represented as a numpy array of zeros
#get_weight will return this vector
self.vec = np.zeros(
dimensions) #creating a vector with the given dimension
# self.storage = [] # creating an empty array for storing vectors
# self.storage.append(self.vec)
"*** YOUR CODE HERE ***"
