Question

我目前正在尝试实现一个概率svm，到目前为止，我对房间的概率有了一个热图，即：

现在，我希望我的观点发生在该情节中，但是当我尝试以自己的方式看时，我得到：

代码看起来像这样：

import numpy as np
import random
from sklearn.svm import SVC
import math
from scipy.optimize import minimize
import matplotlib.pyplot as plt
from sklearn import decomposition
from sklearn import svm
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import warnings
warnings.filterwarnings("ignore")

def training_banana(name):
    inputs = []
    file = open(name, "r")
    for line in file:
        vector = line.split()
        coordinate = []
        for i in range(len(vector)):
            coordinate.append(float(vector[i]))
        inputs.append(coordinate)
    file.close()

    return np.array(inputs)


def define_inputs(name, name_targets):
    inputs = training_banana(name)
    targets_array = training_banana(name_targets)
    N = targets_array.shape[0]
    targets = np.zeros(N)
    for i in range(N):
        targets[i] = targets_array[i][0]
    return inputs, targets, N

#training set
inputs_train, targets_train, N = define_inputs('banana_train.txt', 'banana_train_label.txt')
permute = list(range(N))
random.shuffle(permute)
inputs_train = inputs_train[permute, :]
targets_train = targets_train[permute]

#test set
inputs_test, targets_test, N = define_inputs('banana_test.txt', 'banana_test_label.txt')
permute = list(range(N))
random.shuffle(permute)
inputs_test = inputs_test[permute, :]
targets_test = targets_test[permute]


def plotting():
    ax = plt.gca()
    param_C = [0.01, 0.1, 1, 10, 100]
    param_grid = {'C': param_C, 'kernel': ['poly','rbf', 'linear'], 'gamma': [0.1, 0.01, 0.001, 0.0001]}
    clf = GridSearchCV(SVC(class_weight='balanced'), param_grid)
    clf.fit(inputs_train, targets_train)
    index = clf.best_estimator_.n_support_
    clf = SVC(C=clf.best_params_['C'], cache_size=200, class_weight=None, coef0=0.0,
        decision_function_shape='ovr', degree=5, gamma=clf.best_params_['gamma'], kernel=clf.best_params_['kernel'],
        max_iter=-1, probability=True, random_state=None, shrinking=True,
        tol=0.001, verbose=False)
    clf.fit(inputs_train, targets_train)
    support_vectors = []
    for i in range(len(index)):
        support_vectors.append(inputs_train[i])
    support_vectors = np.array(support_vectors)
    xx = np.linspace(-2, 2, 100)
    yy = np.linspace(-2, 2, 100).T
    xx, yy = np.meshgrid(xx, yy)
    Xfull = np.c_[xx.ravel(), yy.ravel()]
    probabilities = clf.predict_proba(inputs_test)
    predicting_classes_pos_targets = []
    predicting_classes_pos_inputs = []
    predicting_classes_neg_targets = []
    predicting_classes_neg_inputs = []
    prob_mesh = clf.predict_proba(Xfull)
    #print(Xfull)
    print(probabilities)
    for i in range(inputs_test.shape[0]):
        if clf.predict([inputs_test[0]]) == 1:
            predicting_classes_pos_targets.append(1)
            predicting_classes_pos_inputs.append(inputs_test[i])
        else:
            predicting_classes_neg_targets.append(-1)
            predicting_classes_pos_inputs.append(inputs_test[i])
    predicting_classes_pos_inputs = np.array(predicting_classes_pos_inputs)

    #plt.scatter(predicting_classes_pos_inputs[:, 0], predicting_classes_pos_inputs[:, 1], c="b", s=30, cmap=plt.cm.Paired)
    plt.imshow(prob_mesh[:, 0].reshape((100, 100)),
                           extent=(3, 9, 1, 5), origin='lower')
    plt.scatter(predicting_classes_pos_inputs[:, 0], predicting_classes_pos_inputs[:, 1], marker='o', c='w', edgecolor='k')
    #plt.colorbar(imshow_handle, cax=ax, orientation='horizontal')
    plt.show()

任何人都知道如何更改代码以使这些点出现在“概率热图”中吗？

概率SVM，如何在“热图”内绘制点？

0 个答案: