84 lines
2.1 KiB
Python
84 lines
2.1 KiB
Python
import numpy as np
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import datetime
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import random
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from liblinear.liblinearutil import *
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import matplotlib.pyplot as plt
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FILENAME = "hw4_train.dat"
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def read_data(filename):
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with open(filename) as fp:
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lines = fp.readlines()
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x, y = [], []
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for line in lines:
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numbers = [ float(i) for i in line.split() ]
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x.append(numbers[:-1])
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y.append(int(numbers[-1]))
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return x, y
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def format(features):
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'''
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change to LIBSVM format
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'''
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results = []
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for feature in features:
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result = {}
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for index, value in enumerate(feature):
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if value != 0.0:
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result[index+1] = value
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results.append(result)
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return results
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def error(gt, pred):
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err = 0
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for index in range(len(gt)):
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err = (err+1) if gt[index]!=pred[index] else err
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return err/len(gt)
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def new_split(x, y):
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data = list(zip(x, y))
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random.shuffle(data)
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x, y = zip(*data)
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train_x, val_x = x[:120], x[120:]
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train_y, val_y = y[:120], y[120:]
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return (train_x, train_y), (val_x, val_y)
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x, y = read_data(FILENAME)
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x = format(x)
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log_lambda = []
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for i in range(128):
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(train_x, train_y), (val_x, val_y) = new_split(x, y)
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random.seed(datetime.datetime.now().timestamp()+i)
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prob = problem(train_y, train_x)
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lambda_powers = [-6, -4, -2, 0, 2]
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results = []
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for lambda_power in lambda_powers:
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lambda_value = 10 ** lambda_power
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param_C = 1/(2*lambda_value)
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param = parameter('-s 0 -c {} -e 0.000001 -q'.format(param_C))
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model = train(prob, param)
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p_label, p_acc, p_val = predict(val_y, val_x, model)
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err = error(val_y, p_label)
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print("0/1 error: ", err)
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print()
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results.append({'lambda': lambda_power, 'error': err})
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ans, min_err = None, 1
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for i in results:
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if i['error'] <= min_err:
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min_err = i['error']
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ans = i
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print("the largest lambda: {}, log_10(lambda*): {}".format(10**ans['lambda'], ans['lambda']))
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print()
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log_lambda.append(ans['lambda'])
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plt.hist(log_lambda)
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plt.savefig("hw4_11.png")
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