def preprocess(filename):
X, y = load_X_Y.load_X_Y(filename)
X = np.array(X)
y = np.array(y)
rand_s = 20
X, y = shuffle(X, y, random_state=rand_s)
return (X, y)
def preprocess(filename):
X, y = load_X_Y.load_X_Y(filename)
X = np.array(X)
y = np.array(y)
rand_s = 20
X, y = shuffle(X, y, random_state=rand_s)
return X, y
#!/usr/bin/python
# -*- coding: utf-8 -*-
import numpy as np
from sklearn import tree
from sklearn.utils import shuffle
from sklearn.metrics import mean_squared_error
import load_X_Y
import math
import os
###############################################################################
# Load data
parent_path = os.path.dirname(os.getcwd())
data_path = os.path.join(parent_path, 'train_data')
file_in = os.path.join(data_path, 'featureMat_merge_rest_line11.txt')
X, y = load_X_Y.load_X_Y(file_in)
X = np.array(X)
y = np.array(y)
fout = open('iteration.txt', 'w')
fout.write(
"min_samples_leaf\troot_of_mse_of_testset\ttrain_error_rate\ttest_error_rate\n"
)
for m in range(1, 101):
X_train_array = list()
y_train_array = list()
X_test_array = list()
y_test_array = list()
mse_s = list()
train_error_rates = list()
test_error_rates = list()
#!/usr/bin/python
# -*- coding: utf-8 -*-
from sklearn.linear_model import Ridge
from sklearn.preprocessing import PolynomialFeatures
from sklearn.pipeline import make_pipeline
import load_X_Y
from sklearn import preprocessing
import numpy as np
(X,y) = load_X_Y.load_X_Y('featureMat_restday_line10.txt_checked')
for item in X:
for i in range(len(item)):
item[i] = float(item[i])
X_scaled = preprocessing.scale(X)
mat_X_scaled = np.matrix(X_scaled)
weights = np.matrix([0.88181899, 0.03112526, 0.04583346, 0.02779611, 0.01342619])
weights = weights.transpose()
ulti_X = np.array(mat_X_scaled*weights)
clf = make_pipeline(PolynomialFeatures(4), Ridge())
clf.fit(ulti_X, y)
n1 = len(ulti_X)
n2 = len(y)
if(n1 != n2):
print '读取X和Y错误'
#!/usr/bin/python
# -*- coding: utf-8 -*-
from sklearn import neighbors
import load_X_Y
from sklearn import preprocessing
import numpy as np
(X, y) = load_X_Y.load_X_Y('featureMat_restday_line10.txt_checked')
for item in X:
for i in range(len(item)):
item[i] = float(item[i])
X_scaled = preprocessing.scale(X)
mat_X_scaled = np.matrix(X_scaled)
weights = np.matrix(
[0.88181899, 0.03112526, 0.04583346, 0.02779611, 0.01342619])
weights = weights.transpose()
ulti_X = np.array(mat_X_scaled * weights)
clf = neighbors.KNeighborsRegressor(1)
clf.fit(ulti_X, y)
n1 = len(ulti_X)
n2 = len(y)
if (n1 != n2):
print '读取X和Y错误'
#全部样本拿来训练得到的训练集内错误率
numberOfError = 0
import numpy as np import matplotlib.pyplot as plt from sklearn import ensemble from sklearn.utils import shuffle from sklearn.metrics import mean_squared_error import load_X_Y import math import os ############################################################################### # Load data parent_path = os.path.dirname(os.getcwd()) data_path = os.path.join(parent_path, 'train_data') file_in = os.path.join(data_path, 'featureMat_merge_rest_line11.txt') X,y = load_X_Y.load_X_Y(file_in) X = np.array(X) y = np.array(y) X_train_array = list() y_train_array = list() X_test_array = list() y_test_array = list() mse_s = list() train_error_rates = list() test_error_rates = list() feature_importances = list() plt.figure(figsize=(30, 12)) frame = 0 for rand_s in [10, 50, 100, 300, 500, 900, 1400, 4500, 3400, 22] :
#!/usr/bin/python
# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
from sklearn import tree
from sklearn.utils import shuffle
from sklearn.metrics import mean_squared_error
import load_X_Y
import math
###############################################################################
# Load data
X, y = load_X_Y.load_X_Y('featureMat_workday_line10.txt_checked')
X = np.array(X)
y = np.array(y)
rand_s = 20
X, y = shuffle(X, y, random_state=rand_s)
X = X.astype(np.float32)
offset = int(X.shape[0] * 0.9)
X_train, y_train = X[:offset], y[:offset]
X_test, y_test = X[offset:], y[offset:]
###############################################################################
# Fit regression model
clf = tree.DecisionTreeRegressor()
clf = clf.fit(X_train, y_train)
mse = mean_squared_error(y_test, clf.predict(X_test))
