GeezerVison is showing github.com — ClassicComputerScienceProblemsInPython/Chapter7/iris_test.py at master · yazmin5/ClassicComputerScienceProblemsInPython

This repository was archived by the owner on Nov 14, 2020. It is now read-only.

Expand file tree

iris_test.py

Copy path

More file actions

Latest commit

History

64 lines (57 loc) · 2.57 KB

iris_test.py

Copy path

File metadata and controls

64 lines (57 loc) · 2.57 KB

Raw

Copy raw file

Download raw file

Open symbols panel

Edit and raw actions

# iris_test.py

# From Classic Computer Science Problems in Python Chapter 7

# 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.

import csv

from typing import List

from util import normalize_by_feature_scaling

from network import Network

from random import shuffle

if __name__ == "__main__":

iris_parameters: List[List[float]] = []

iris_classifications: List[List[float]] = []

iris_species: List[str] = []

with open('iris.csv', mode='r') as iris_file:

irises: List = list(csv.reader(iris_file))

shuffle(irises) # get our lines of data in random order

for iris in irises:

parameters: List[float] = [float(n) for n in iris[0:4]]

iris_parameters.append(parameters)

species: str = iris[4]

if species == "Iris-setosa":

iris_classifications.append([1.0, 0.0, 0.0])

elif species == "Iris-versicolor":

iris_classifications.append([0.0, 1.0, 0.0])

else:

iris_classifications.append([0.0, 0.0, 1.0])

iris_species.append(species)

normalize_by_feature_scaling(iris_parameters)

iris_network: Network = Network([4, 6, 3], 0.3)

def iris_interpret_output(output: List[float]) -> str:

if max(output) == output[0]:

return "Iris-setosa"

elif max(output) == output[1]:

return "Iris-versicolor"

else:

return "Iris-virginica"

# train over the first 140 irises in the data set 50 times

iris_trainers: List[List[float]] = iris_parameters[0:140]

iris_trainers_corrects: List[List[float]] = iris_classifications[0:140]

for _ in range(50):

iris_network.train(iris_trainers, iris_trainers_corrects)

# test over the last 10 of the irises in the data set

iris_testers: List[List[float]] = iris_parameters[140:150]

iris_testers_corrects: List[str] = iris_species[140:150]

iris_results = iris_network.validate(iris_testers, iris_testers_corrects, iris_interpret_output)

print(f"{iris_results[0]} correct of {iris_results[1]} = {iris_results[2] * 100}%")