Python plot_results_linearの例

コード例 #1

# example_regression.py

import matplotlib.pyplot as plt
import numpy as np
import plot_results

def example(nfeature,m):
	X = 0.5+3.5*np.random.rand(nfeature,m)
	Y = np.absolute(0.3*X + 0.25 + 0.2*np.random.randn(nfeature,m))
	return X,Y

if __name__ == "__main__":
	np.random.seed(100)
	nfeature = 1
	nsample = 500
	X,Y = example(nfeature,nsample)
	plot_results.plot_results_linear(X,Y)
	plt.show()

コード例 #2

ファイル: driver_linearregression_logcosh.py プロジェクト: satishchandrareddy/IntroML

# driver_linearregression_logcosh.py
# Run in IntroML/Code/Version1.2

import LRegression
import matplotlib.pyplot as plt
import numpy as np
import Optimizer
import plot_results

# (1) Set up data
m = 1000
X = np.random.rand(1, m)
Y = 0.5 * X + 0.25
Y = Y + 0.1 * np.random.randn(m)
# (2) Define model
model = LRegression.LRegression(1, "linear")
# (3) Compile model - use logcosh loss function
optimizer = Optimizer.GradientDescent(0.5)
model.compile("logcosh", optimizer)
# (4) Train model
epochs = 50
history = model.fit(X, Y, epochs)
# (5) Results
# plot loss and accuracy
plot_results.plot_results_history(history, ["loss"])
plot_results.plot_results_history(history, ["accuracy"])
# plot results
plot_results.plot_results_linear(model, X, Y)
plt.show()