perpy 0.1.13

Functions commonly used in computer paper writing and scientific research.

PERPY——Functions commonly used in computer paper writing and scientific research.

INSTALL

pip install perpy

IMPORT

import perpy as py

LOAD——load dataset from file

TEST FILE——test1.txt

1 5 0
2 4 0
3 3 0
4 2 1
5 1 1

CASE 1——Non label & No max min scaling

path = r'D:\perpy' # file directory
col_labels = None # non label
scaling = False # no max min scaling

x= py.load(path, col_labels, scaling)

print(x)

CASE 2——The labels is in the first column & To max min scaling

path = r'D:\perpy' # file directory
col_labels = 0 # the labels is in the first column
scaling = True # to max min scaling

x, r = py.load(path, col_labels, scaling)

print(x, '\n', r)

CASE 3——The labels is in the last column & No max min scaling

path = r'D:\perpy' # file directory
col_labels = 2 # the labels is in the last column. non-zero number
scaling = False # no max min scaling

x, r = py.load(path, col_labels, scaling)

print(x, '\n', r)

DIST——Calculate the euclidean distance between point A and point B

A = np.mat([1,2,3,4]) # point A
B = np.mat([4,3,2,1]) # point B

dist = py.dist(A, B)

print(dist)

4.47213595499958

PLT_SCATTER——Drawing scatter plot

path = r'D:\perpy' # file directory
col_labels = 2 # the labels is in the last column. non-zero number
scaling = False # no max min scaling

x, r = py.load(path, col_labels, scaling)

py.plt_scatter(x=x, labels=r, fig_label=['X——label','Y——label'], fig_legend=['Cluster','01']) # 00-upper left, 01-upper right, 10-down left, 11-down right

PLT_RUNTIME——Drawing runtime plot

times = [[1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0], # Runtime list, times[0] is A times, times[1] is B times.
        [1,1.1,1.2,1.3,1.4,1.5,1.6,1.7]]
instances = [1,2,3,4,5,6,7,8] # x
labels = ['$A$','$B$'] # labels

py.plt_runtime(times, instances, labels)

PLT_RADAR——Drawing radar plot

TEST FILE——test2.txt

1.0 0.8 0.6 0.5 0.9
0.6 0.9 0.7 0.7 0.3
0.4 0.1 1.0 0.8 0.5

data = np.loadtxt(r'D:\perpy\test2.txt')
algorithm = ['a', 'b','c']
labels = np.array(['A','B','C','D','E'])

py.plt_radar(labels, data, algorithm, legend=(1.7,0.68))