def clusterDistance(cluster1, cluster2, distance_agg=min):
"""finds the aggregate distance between elements of cluster1
and elements of cluster2"""
return distance_agg([
distance(input1, input2) for input1 in getValues(cluster1)
for input2 in getValues(cluster2)
])
def knnClassify(k, labeledPoints, newPoint):
byDistance = sorted(
labeledPoints,
key=lambda pointlabel: distance(pointlabel[0], newPoint))
kNearestLabels = [label for _, label in byDistance[:k]]
return majorityVote(kNearestLabels)
def find_eigenvector(A, tolerance=0.00001):
guess = [1 for __ in A]
while True:
result = matrix_operate(A, guess)
length = magnitude(result)
next_guess = scalarMultiply(1 / length, result)
if distance(guess, next_guess) < tolerance:
return next_guess, length # eigenvector, eigenvalue
guess = next_guess
def find_eigenvector(A, tolerance=0.00001):
guess = [1 for __ in A]
while True:
result = matrix_operate(A, guess)
length = magnitude(result)
next_guess = scalarMultiply(1/length, result)
if distance(guess, next_guess) < tolerance:
return next_guess, length # eigenvector, eigenvalue
guess = next_guess
def knnClassify(k, labeledPoints, newPoint):
byDistance = sorted(labeledPoints, key=lambda pointlabel: distance(pointlabel[0], newPoint))
kNearestLabels = [label for _, label in byDistance[:k]]
return majorityVote(kNearestLabels)
def clusterDistance(cluster1, cluster2, distance_agg=min):
"""finds the aggregate distance between elements of cluster1
and elements of cluster2"""
return distance_agg([distance(input1, input2)
for input1 in getValues(cluster1)
for input2 in getValues(cluster2)])
