本文整理汇总了Python中loader.Loader.load_data方法的典型用法代码示例。如果您正苦于以下问题：Python Loader.load_data方法的具体用法？Python Loader.load_data怎么用？Python Loader.load_data使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类loader.Loader的用法示例。
在下文中一共展示了Loader.load_data方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: Loader
# 需要导入模块: from loader import Loader [as 别名]
# 或者: from loader.Loader import load_data [as 别名]
from __future__ import print_function
from loader import Loader
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
ld = Loader()
plt_X = []
plt_Y = []
[X, y] = ld.load_data('datasets/train.csv')
for n_clusters in range(1, 100):
km = KMeans(n_clusters=n_clusters)
km.fit(X)
plt_X.append(n_clusters)
plt_Y.append(km.inertia_)
plt.plot(plt_X, plt_Y)
plt.ylabel('Within groups sum of squares')
plt.xlabel('Number of Clusters')
plt.savefig('figures/' + 'Raw_Kmeans_Curious_George_Elbow_Curve.png')
示例2: Loader
# 需要导入模块: from loader import Loader [as 别名]
# 或者: from loader.Loader import load_data [as 别名]
plt.scatter(px, py, c=colors[i])
plt.xlabel(x_label)
plt.ylabel(y_label)
plt.title(name)
plt.savefig('figures/' + name.replace(' ', '_') + classifier + '.png')
figure_identifier.clf()
plt.close(figure_identifier)
ld = Loader()
#np.random.seed(5)
centers = [[1, 1], [-1, -1], [1, -1]]
[X, y] = ld.load_data('datasets/Curious_George_train_features_100_percent.csv')
[X_test, y_test] = ld.load_data('datasets/Curious_George_test_features.csv')
# You are to implement (or find the code for) six algorithms. The first two are clustering algorithms:
# k-means clustering
# Expectation Maximization
# You can choose your own measures of distance/similarity. Naturally, you'll have to justify your choices, but you're practiced at that sort of thing by now.
# The last four algorithms are dimensionality reduction algorithms:
# PCA
# ICA
# Randomized Projections示例3: Loader
# 需要导入模块: from loader import Loader [as 别名]
# 或者: from loader.Loader import load_data [as 别名]
plt.xticks(())
plt.yticks(())
plt.title(name)
plt.legend(loc='lower right', prop=dict(size=12))
plt.savefig('figures/' + title.replace(' ', '_') + '_Training_results.png')
#plt.show()
ld = Loader()
#np.random.seed(5)
centers = [[1, 1], [-1, -1], [1, -1]]
[X, y] = ld.load_data('datasets/Live_Television.train_features_100_percent.csv')
[X_test, y_test] = ld.load_data('datasets/Live_Television.test_features.csv')
# You are to implement (or find the code for) six algorithms. The first two are clustering algorithms:
# Expectation Maximization
# The last four algorithms are dimensionality reduction algorithms:
# PCA
# ICA
# Randomized Projections
# Any other feature selection algorithm you desire
# You are to run a number of experiments. Come up with at least two datasets. If you'd like (and it makes a lot of sense in this case) you can use the ones you used in the first assignment.
# Run the clustering algorithms on the data sets and describe what you see.
# Apply the dimensionality reduction algorithms to the two datasets and describe what you see.示例4: Loader
# 需要导入模块: from loader import Loader [as 别名]
# 或者: from loader.Loader import load_data [as 别名]
from __future__ import print_function
from loader import Loader
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
ld = Loader()
plt_X = []
plt_Y = []
[X, y] = ld.load_data("datasets/Curious_George_train_features_100_percent.csv")
for n_clusters in range(1, 16):
km = KMeans(n_clusters=n_clusters)
km.fit(X)
plt_X.append(n_clusters)
plt_Y.append(km.inertia_)
plt.plot(plt_X, plt_Y)
plt.ylabel("Within groups sum of squares")
plt.xlabel("Number of Clusters")
plt.savefig("figures/" + "Kmeans_Curious_George_Elbow_Curve.png")