43 lines
1.3 KiB
Python
43 lines
1.3 KiB
Python
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"""Prepares the dataset for training"""
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import numpy as np
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from .normalize import normalize
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from .generate_sinusoids import generate_sinusoids
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from .generate_polynomials import generate_polynomials
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def prepare_for_training(data, polynomial_degree=0, sinusoid_degree=0, normalize_data=True):
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# 计算样本总数
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num_examples = data.shape[0]
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data_processed = np.copy(data)
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# 预处理
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features_mean = 0
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features_deviation = 0
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data_normalized = data_processed
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if normalize_data:
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(
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data_normalized,
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features_mean,
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features_deviation
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) = normalize(data_processed)
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data_processed = data_normalized
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# 特征变换sinusoidal
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if sinusoid_degree > 0:
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sinusoids = generate_sinusoids(data_normalized, sinusoid_degree)
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data_processed = np.concatenate((data_processed, sinusoids), axis=1)
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# 特征变换polynomial
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if polynomial_degree > 0:
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polynomials = generate_polynomials(data_normalized, polynomial_degree, normalize_data)
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data_processed = np.concatenate((data_processed, polynomials), axis=1)
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# 加一列1
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data_processed = np.hstack((np.ones((num_examples, 1)), data_processed))
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return data_processed, features_mean, features_deviation
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