-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathgeneration_test.py
More file actions
51 lines (42 loc) · 1.56 KB
/
generation_test.py
File metadata and controls
51 lines (42 loc) · 1.56 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
from datagen import NumericData
from datagen import ClassroomData
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
def linear_data_example():
# sample usage
data = NumericData.generate_data_linear(0, 1, 150, 2, 5, 1)
plt.scatter(data[0], data[1], alpha=0.8,
edgecolors='none', c='blue')
plt.show()
def cluster_data_example():
# visualize 2D data clusters
data = NumericData.generate_data_cluster(num_points=500, sample_balance=100, k=5, random_factor=15)
x_data = data[0].T
plt.scatter(x_data[0], x_data[1], c=data[1],
alpha=0.8, edgecolors='none',
cmap=plt.cm.get_cmap('Spectral', 5))
plt.colorbar()
plt.show()
def exam_data_example():
# visualize exam scores
data = ClassroomData.generate_data_exams(num_students=5, num_exams=6, trend_up=20, exam_var=6)
names = data[0]
data = np.delete(data, 0, 0)
data = data.T
df = pd.DataFrame({'Exam': range(1, 7),
names[0]: data[0].astype(np.float),
names[1]: data[1].astype(np.float),
names[2]: data[2].astype(np.float),
names[3]: data[3].astype(np.float),
names[4]: data[4].astype(np.float)})
for column in df.drop('Exam', axis=1):
plt.plot(df['Exam'], df[column], label=column, alpha=0.8)
plt.gca().set_ylim([0, 100])
plt.xlabel("Exam Number")
plt.ylabel("Exam Score")
plt.legend(loc=4)
plt.show()
exam_data_example()
linear_data_example()
cluster_data_example()