技術標籤：人工智慧資料分析機器學習

機器學習筆記part1
1,epochs(時代/學習週期)→模型通過不斷地執行(學習)→不斷的更新coefficient(係數)→更好地擬合數據
即b=b-learningrate* error * x
2把所有地epoch(學習週期，注意，每個epoch裡會有相應的訓練集)進行loop(迴圈迭代)
3每一次係數(coefficient)迴圈都會進行係數調優

error=prediction-expected

// An highlighted block
from Ay_hat import make_prediction#模組化程式設計，輕量化程式碼








def using_sgd_method_to_calculate_coefficient
 
(traing_dataset,learning_rate,n_times_epoch):
	coefficients=[0.0 for i in range(len(traing_dataset[0]))]
	for epoch in range(n_times_epoch):
		the_sum_of_error = 0#用於計數
		for row in traing_dataset:
			y_hat=make_prediction(row,coefficients)
			error = y_hat - row[-1]#error=prediction-expected
			the_sum_of_error +=
 
 error**2#用平方避免負數的情況出現
			coefficients[0] = coefficients[0] - learning_rate*error#b=b-learningrate*errorx
			for i in range(len(row)-1):
				coefficients[i+1] = coefficients[i+1] - learning_rate*error*row[i]

		print("This is epoch :",epoch,"the learning_rate we are using is :",learning_rate,
 
"the error is :",the_sum_of_error)

	return coefficients




your_training_dataset=[[1,1],[2,3],[4,3],[3,2],[5,5]]
test_coefficients = [0.4,0.8]

your_model_learning_rate = 0.01
your_n_epoch = 50
your_coefficient = using_sgd_method_to_calculate_coefficient(your_training_dataset,your_model_learning_rate,your_n_epoch)