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# util.py
# From Classic Computer Science Problems in Python Chapter 7
# Copyright 2018 David Kopec
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List
from math import exp
# dot product of two vectors
def dot_product(xs: List[float], ys: List[float]) -> float:
return sum(x * y for x, y in zip(xs, ys))
# the classic sigmoid activation function
def sigmoid(x: float) -> float:
return 1.0 / (1.0 + exp(-x))
def derivative_sigmoid(x: float) -> float:
sig: float = sigmoid(x)
return sig * (1 - sig)
# assume all rows are of equal length
# and feature scale each column to be in the range 0 - 1
def normalize_by_feature_scaling(dataset: List[List[float]]) -> None:
for col_num in range(len(dataset[0])):
column: List[float] = [row[col_num] for row in dataset]
maximum = max(column)
minimum = min(column)
for row_num in range(len(dataset)):
dataset[row_num][col_num] = (dataset[row_num][col_num] - minimum) / (maximum - minimum)