forked from davecom/ClassicComputerScienceProblemsInPython
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathiris_test.py
More file actions
64 lines (57 loc) · 2.57 KB
/
Copy pathiris_test.py
File metadata and controls
64 lines (57 loc) · 2.57 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
# iris_test.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.
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}%")