PriitJ: Uus lehekülg: '

 import csv from collections import defaultdict  # https://github.com/sjwhitworth/golearn/blob/master/examples/datasets/tennis.csv with open("tennis.csv") as f:     r = csv....'

2019-04-11T13:32:50Z

Uus lehekülg: '<pre> import csv from collections import defaultdict # https://github.com/sjwhitworth/golearn/blob/master/examples/datasets/tennis.csv with open("tennis.csv") as f: r = csv....'

Uus lehekülg

<pre>
import csv
from collections import defaultdict

# https://github.com/sjwhitworth/golearn/blob/master/examples/datasets/tennis.csv
with open("tennis.csv") as f:
r = csv.reader(f, delimiter=",")
samples = [s for s in r]

#print(samples)

N = len(samples[1:])
N_n = 0
N_y = 0

for row in samples[1:]:
if row[4] == "yes" : N_y += 1
elif row[4] == "no" : N_n += 1

cpt_y = {}
cpt_n = {}
for i in range(4):
cpt_y[i] = defaultdict(int)
cpt_n[i] = defaultdict(int)

for row in samples[1:]:
for i in range(4):
if row[4] == "yes" :
cpt_y[i][row[i]] += 1
elif row[4] == "no" :
cpt_n[i][row[i]] += 1

#print(N, "yes", N_y, "no", N_n)

uniques = [3, 3, 2, 2]
def predict(rida):
H_yes = (N_y/N)
H_no = (N_n/N)
for i in range(4):
H_yes *= ((cpt_y[i][rida[i]] + 1) / (N_y +uniques[i]))
H_no *= ((cpt_n[i][rida[i]] + 1) / (N_n +uniques[i]))
print("H_yes", H_yes)
print("H_no", H_no)
print("Yes?", H_yes>H_no)

predict(["overcast", "cool", "high", "true"])
</pre>

Iti0210w92 demo - Redigeerimiste ajalugu

PriitJ: Uus lehekülg: '
import csv from collections import defaultdict # https://github.com/sjwhitworth/golearn/blob/master/examples/datasets/tennis.csv with open("tennis.csv") as f: r = csv....'