def feed_data(self, x, y, sample_weight=None):
#
if sample_weight is not None:
sample_weight = np.asarray(sample_weight)
x, y, _, features, feat_dicts, label_dict = DataUtil.quantize_data(x, y, wc=np.array([False] * len(x[0])))
# 利用Numpy中bincount方法，获得各类别数据的个数；
cat_counter = np.bincount(y)
# 记录各维度特征的取值个数；
n_possibilities = [len(feats) for feats in features]
# 获得各类别数据的下标；
labels = [y == value for value in range(len(cat_counter))]
# 利用下标获取按类别分开后的输入数据的数组；
labelled_x = [x[ci].T for ci in labels]
# 更新模型的各个属性；
self._x, self._y = x, y
self._labelled_x, self._label_zip = labelled_x, list(zip(labels, labelled_x))
self._cat_counter, self._feat_dicts, self._n_possibilities = cat_counter, feat_dicts, n_possibilities
self.label_dict = label_dict
# 调用处理样本权重的函数，以更新记录条件概率的数组；
self.feed_sample_weight(sample_weight)
def feed_data(self, x, y, sample_weight=None):
if sample_weight is not None:
sample_weight = np.asarray(sample_weight)
x, y, wc, features, feat_dics, label_dic = DataUtil.quantize_data(
x, y, wc=self._whether_continuous, separate=True)
if self._whether_continuous is None:
self._whether_continuous = wc
self._whether_discrete = ~self._whether_continuous
self.label_dic = label_dic
discrete_x, continuous_x = x
cat_counter = np.bincount(y)
self._cat_counter = cat_counter
labels = [y == value for value in range(len(cat_counter))]
labelled_x = [discrete_x[ci].T for ci in labels]
self._multinomial._x, self._multinomial._y = x, y
self._multinomial._labelled_x, self._multinomial._label_zip = labelled_x, list(zip(labels, labelled_x))
self._multinomial._cat_counter = cat_counter
self._multinomial._feat_dics = [dic for i, dic in enumerate(feat_dics) if self._whether_discrete[i]]
self._multinomial._n_possibilities = [len(feats) for i, feats in enumerate(features)
if self._whether_discrete[i]]
self._multinomial.label_dic = label_dic
labelled_x = [continuous_x[label].T for label in labels]
self._gaussian._x, self._gaussian._y = continuous_x.T, y
self._gaussian._labelled_x, self._gaussian._label_zip = labelled_x, labels
self._gaussian._cat_counter, self._gaussian.label_dic = cat_counter, label_dic
self.feed_sample_weight(sample_weight)
def feed_data(self, x, y, sample_weight=None):
if sample_weight is not None:
sample_weight = np.asarray(sample_weight)
x, y, wc, features, feat_dicts, label_dict = DataUtil.quantize_data(
x, y, wc=self._whether_continuous, separate=True)
if self._whether_continuous is None:
self._whether_continuous = wc
self._whether_discrete = ~self._whether_continuous
self.label_dict = label_dict
discrete_x, continuous_x = x
cat_counter = np.bincount(y)
self._cat_counter = cat_counter
labels = [y == value for value in range(len(cat_counter))]
labelled_x = [discrete_x[ci].T for ci in labels]
self._multinomial._x, self._multinomial._y = x, y
self._multinomial._labelled_x, self._multinomial._label_zip = labelled_x, list(zip(labels, labelled_x))
self._multinomial._cat_counter = cat_counter
self._multinomial._feat_dicts = [dic for i, dic in enumerate(feat_dicts) if self._whether_discrete[i]]
self._multinomial._n_possibilities = [len(feats) for i, feats in enumerate(features)
if self._whether_discrete[i]]
self._multinomial.label_dict = label_dict
labelled_x = [continuous_x[label].T for label in labels]
self._gaussian._x, self._gaussian._y = continuous_x.T, y
self._gaussian._labelled_x, self._gaussian._label_zip = labelled_x, labels
self._gaussian._cat_counter, self._gaussian.label_dict = cat_counter, label_dict
self.feed_sample_weight(sample_weight)
def feed_data(self, x, y, sample_weight=None):
if sample_weight is not None:
sample_weight = np.asarray(sample_weight)
x, y, _, features, feat_dicts, label_dict = DataUtil.quantize_data(x, y, wc=np.array([False] * len(x[0])))
cat_counter=np.bincount(y) #统计两个类别的个数
n_possibilities = [len(feats) for feats in features] #记录各维度特征的取值个数
labels = [y == value for value in range(len(cat_counter)) ]#获取各类别的数据的下标
labelled_x = [x[ci].T for ci in labels]
self._x, self._y = x, y
self._labelled_x, self._label_zip = labelled_x, list(zip(labels, labelled_x))
self._cat_counter, self._feat_dicts, self._n_possibilities = cat_counter, feat_dicts, n_possibilities
self.label_dict = label_dict
self.feed_sample_weight(sample_weight)
def feed_data(self, x, y, sample_weight=None):
if sample_weight is not None:
sample_weight = np.asarray(sample_weight)
x, y, _, features, feat_dicts, label_dict = DataUtil.quantize_data(x, y, wc=np.array([False] * len(x[0])))
cat_counter = np.bincount(y)
n_possibilities = [len(feats) for feats in features]
labels = [y == value for value in range(len(cat_counter))]
labelled_x = [x[ci].T for ci in labels]
self._x, self._y = x, y
self._labelled_x, self._label_zip = labelled_x, list(zip(labels, labelled_x))
self._cat_counter, self._feat_dicts, self._n_possibilities = cat_counter, feat_dicts, n_possibilities
self.label_dict = label_dict
self.feed_sample_weight(sample_weight)