在pycharm中運(yùn)行的。。。

# Quick Start

1、第一個(gè)

```

from svmutil import *

# Read data in LIBSVM format

y, x = svm_read_problem('../heart_scale')

m = svm_train(y[:200], x[:200], '-c 4')

p_label, p_acc, p_val = svm_predict(y[200:], x[200:], m)

```

2、第二個(gè)

```

# Construct problem in python format

# Dense data

# y, x = [1,-1], [[1,0,1], [-1,0,-1]]

# Sparse data

y, x = [1,-1], [{1:1, 3:1}, {1:-1,3:-1}]

prob? = svm_problem(y, x)

param = svm_parameter('-t 0 -c 4 -b 1')

m = svm_train(prob, param)

p_label, p_acc, p_val = svm_predict(y, x, m)

```

3、第三個(gè)

```

# 4

# Precomputed kernel data (-t 4)

# # Dense data

# # y, x = [1,-1], [[1, 2, -2], [2, -2, 2]]

# # Sparse data

# y, x = [1,-1], [{0:1, 1:2, 2:-2}, {0:2, 1:-2, 2:2}]

# # isKernel=True must be se for precomputed kernel

prob? = svm_problem(y, x, isKernel=True)

param = svm_parameter('-t 4 -c 4 -b 1')

m = svm_train(prob, param)

p_label, p_acc, p_val = svm_predict(y, x, m)

# For the format of precomputed kernel, please read LIBSVM README.

```

4、第四個(gè)

```

# Other utility functions

svm_save_model('heart_scale.model', m)

m = svm_load_model('heart_scale.model')

p_label, p_acc, p_val = svm_predict(y, x, m, '-b 1')

ACC, MSE, SCC = evaluations(y, p_label)

```

5、第五個(gè)

```

# Getting online help

help(svm_train)

```

6、第六個(gè)

```

from svm import *

prob = svm_problem([1,-1], [{1:1, 3:1}, {1:-1,3:-1}])

param = svm_parameter('-c 4')

m = libsvm.svm_train(prob, param) # m is a ctype pointer to an svm_model

# Convert a Python-format instance to svm_nodearray, a ctypes structure

x0, max_idx = gen_svm_nodearray({1:1, 3:1})

label = libsvm.svm_predict(m, x0)

print(label)

```