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svm.py
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#!/usr/bin/env python
from ctypes import *
from ctypes.util import find_library
from os import path
import sys
from sys import platform
dirname = path.dirname(path.abspath(__file__))
if platform == "linux" or platform == "linux2":
shared_library_name = "libthundersvm.so"
elif platform == "win32":
shared_library_name = "thundersvm.dll"
elif platform == "darwin":
shared_library_name = "libthundersvm.dylib"
else :
print ("OS not supported!")
exit()
if path.exists(path.abspath(path.join(dirname, shared_library_name))):
lib_path = path.abspath(path.join(dirname, shared_library_name))
else:
if platform == "linux" or platform == "linux2":
lib_path = path.join(dirname, '../build/lib/', shared_library_name)
elif platform == "win32":
lib_path = path.join(dirname, '../build/bin/Debug/', shared_library_name)
elif platform == "darwin":
lib_path = path.join(dirname, '../build/lib/', shared_library_name)
if path.exists(lib_path):
thundersvm = CDLL(lib_path)
else :
print ("Please build the library first!")
exit()
dataset_path = dirname
'''
class dataset(object):
def __init__(self):
self.obj = lib.DataSet_new()
def load_from_python(self, arg1, arg2, arg3):
lib.DataSet_load_from_python(self.obj, arg1, arg2, arg3)
def svm_train(param):
param_list = param.split()
param_list.insert(0, 'thundersvm-train')
param_array = (c_char_p * len(param_list))()
param_array[:] = param_list
thundersvm.thundersvm_train(len(param_list), param_array)
def svm_predict(param):
param_list = param.split()
param_list.insert(0, 'thundersvm-predict')
param_array = (c_char_p * len(param_list))()
param_array[:] = param_list
thundersvm.thundersvm_predict(len(param_list), param_array)
'''
def svm_read_problem(data_file_name):
"""
svm_read_problem(data_file_name) -> [y, x]
Read LIBSVM-format data from data_file_name and return labels y
and data instances x.
"""
prob_y = []
prob_x = []
file_path = data_file_name
for line in open(file_path):
line = line.split(None, 1)
# In case an instance with all zero features
if len(line) == 1: line += ['']
label, features = line
xi = features.encode('utf-8')[:-1]
#for e in features.split():
# ind, val = e.split(":")
# xi[int(ind)] = float(val)
prob_y += [float(label)]
prob_x += [xi]
return (prob_y, prob_x)
def svm_train(arg1, arg2 = None, arg3 = None, arg4 = None):
if arg2:
arg1_array = (c_float * len(arg1))()
arg1_array[:] = arg1
#arg2_string_list = [str(d).encode('utf-8')[1:-1] for d in arg2]
arg2_string_list = arg2
arg2_array = (c_char_p * len(arg2_string_list))()
arg2_array[:] = arg2_string_list
#print(arg1_array[0])
#print(arg2_array[0])
arg4_list = arg4.encode('utf-8').split()
arg4_array = (c_char_p * len(arg4_list))()
arg4_array[:] = arg4_list
#dataset_python = dataset();
#dataset_python.load_from_python(arg1, arg2, arg3)
#print(dataset_python)
thundersvm.load_from_python_interface(arg1_array, arg2_array, len(arg1_array))
thundersvm.thundersvm_train_after_parse(arg4_array, len(arg4_array), arg3.encode('utf-8'))
else:
param_list = arg1.encode('utf-8').split()
param_list.insert(0, 'thundersvm-train')
param_array = (c_char_p * len(param_list))()
param_array[:] = param_list
#print(param_array[0])
thundersvm.thundersvm_train(len(param_list), param_array)
def svm_predict(arg1, arg2 = None, arg3 = None, arg4 = None, arg5 = None):
if arg2:
arg1_array = (c_float * len(arg1))()
arg1_array[:] = arg1
arg2_array = (c_char_p * len(arg2))()
arg2_array[:] = arg2
thundersvm.load_from_python_interface(arg1_array, arg2_array, len(arg1_array))
if arg5:
arg5_list = arg5.encode('utf-8').split()
arg5_array = (c_char_p * len(arg5_list))()
arg5_array[:] = arg5_list
thundersvm.thundersvm_predict_after_parse(arg3.encode('utf-8'), arg4.encode('utf-8'), arg5_array, len(arg5_array))
else :
arg5_array = None
thundersvm.thundersvm_predict_after_parse(arg3.encode('utf-8'), arg4.encode('utf-8'), arg5_array, 0)
else:
param_list = arg1.split()
param_list.insert(0, 'thundersvm-predict')
param_array = (c_char_p * len(param_list))()
param_array[:] = param_list
thundersvm.thundersvm_predict(len(param_list), param_array)
#libsvm.thundersvm_train(15, "./thundersvm-train -s 1 -t 2 -g 0.5 -c 100 -n 0.1 -e 0.001 dataset/test_dataset.txt dataset/test_dataset.txt.model");