Distance Functions (Euclidean+ Notebook) by antmarakis · Pull Request #460 · aimacode/aima-python · GitHub
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239 changes: 238 additions & 1 deletion learning.ipynb
28 changes: 16 additions & 12 deletions learning.py
Original file line number Diff line number Diff line change
Expand Up @@ -17,28 +17,32 @@
# ______________________________________________________________________________


def rms_error(predictions, targets):
return math.sqrt(ms_error(predictions, targets))
def euclidean_distance(X, Y):
return math.sqrt(sum([(x - y)**2 for x, y in zip(X, Y)]))


def ms_error(predictions, targets):
return mean([(p - t)**2 for p, t in zip(predictions, targets)])
def rms_error(X, Y):
return math.sqrt(ms_error(X, Y))


def mean_error(predictions, targets):
return mean([abs(p - t) for p, t in zip(predictions, targets)])
def ms_error(X, Y):
return mean([(x - y)**2 for x, y in zip(X, Y)])


def manhattan_distance(predictions, targets):
return sum([abs(p - t) for p, t in zip(predictions, targets)])
def mean_error(X, Y):
return mean([abs(x - y) for x, y in zip(X, Y)])


def mean_boolean_error(predictions, targets):
return mean(int(p != t) for p, t in zip(predictions, targets))
def manhattan_distance(X, Y):
return sum([abs(x - y) for x, y in zip(X, Y)])


def hamming_distance(predictions, targets):
return sum(p != t for p, t in zip(predictions, targets))
def mean_boolean_error(X, Y):
return mean(int(x != y) for x, y in zip(X, Y))


def hamming_distance(X, Y):
return sum(x != y for x, y in zip(X, Y))

# ______________________________________________________________________________

Expand Down
39 changes: 36 additions & 3 deletions tests/test_learning.py