# 创建一个简单的机器学习模型

2021年5月12日21:28:36 发表评论 872 次浏览

``````# python library to generate random numbers
from random import randint

# the limit within which random numbers are generated
TRAIN_SET_LIMIT = 1000

# to create exactly 100 data items
TRAIN_SET_COUNT = 100

# list that contains input and corresponding output
TRAIN_INPUT = list ()
TRAIN_OUTPUT = list ()

# loop to create 100 data  items with three columns each
for i in range (TRAIN_SET_COUNT):
a = randint( 0 , TRAIN_SET_LIMIT)
b = randint( 0 , TRAIN_SET_LIMIT)
c = randint( 0 , TRAIN_SET_LIMIT)

# creating the output for each data item
op = a + ( 2 * b) + ( 3 * c)
TRAIN_INPUT.append([a, b, c])

# adding each output to output list
TRAIN_OUTPUT.append(op)``````

1.

2.

``````# Sk-Learn contains the linear regression model
from sklearn.linear_model import LinearRegression

# Initialize the linear regression model
predictor = LinearRegression(n_jobs = - 1 )

# Fill the Model with the Data
predictor.fit(X = TRAIN_INPUT, y = TRAIN_OUTPUT)``````

``````# Random Test data
X_TEST = [[ 10, 20, 30 ]]

# Predict the result of X_TEST which holds testing data
outcome = predictor.predict(X = X_TEST)

# Predict the coefficients
coefficients = predictor.coef_

# Print the result obtained for the test data
print( 'Outcome : {}\nCoefficients : {}' .format(outcome, coefficients))``````

10 + 20 * 2 + 30 * 3 = 140。

``````Outcome : [ 140.]
Coefficients : [ 1. 2. 3.]``````