本文整理汇总了Python中layer.Layer._predict方法的典型用法代码示例。如果您正苦于以下问题：Python Layer._predict方法的具体用法？Python Layer._predict怎么用？Python Layer._predict使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类layer.Layer的用法示例。
在下文中一共展示了Layer._predict方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
# 需要导入模块: from layer import Layer [as 别名]
# 或者: from layer.Layer import _predict [as 别名]
class LayerBuilder:
def __init__(self, X, Y, num_nodes=50, num_iter=100, epsilon=0.01,
test_size=0.3, boostCV_size=0.2, nodeCV_size=0.1, boost_decay=False,
ultra_boosting=False, g_final=0.0000001, g_tol=0.01,
threshold=-0.01, minibatch=False, validation=SHUFFLED,
symmetric_labels=False, mode=REGRESSION, alpha=0.0):
print "creating training, validation, and testing sets..."
train_test = train_test_split(X, Y, test_size=test_size)
X_nottest, X_test, Y_nottest, Y_test = train_test
print 'fitting scalers...tranforming data...'
if symmetric_labels:
X_nottest, X_nottest_inds = FoldLabels(X_nottest)
X_test, X_test_inds = FoldLabels(X_test)
X_nottest, X_nottest_scaler = Preprocess(X_nottest)
X_test, _ = Preprocess(X_test, Scaler=X_nottest_scaler)
Y_nottest, Y_nottest_scaler = Preprocess(Y_nottest)
(self.X_train,
self.X_validate_layer,
self.Y_train,
self.Y_validate_layer) = train_test_split(X_nottest, Y_nottest, test_size=boostCV_size)
if validation == UNIFORM:
(self.X_train_node,
self.X_validate_node,
self.Y_train_node,
self.Y_validate_node) = train_test_split(self.X_train, self.Y_train,
test_size=nodeCV_size)
elif validation == SHUFFLED:
self.X_train_node = self.X_train
self.X_validate_node = self.X_validate_layer
self.Y_train_node = self.Y_train
self.Y_validate_node = self.Y_validate_layer
else:
raise ValueError("What is this validation supposed to mean -.-'")
self.init_layer(num_iter, alpha, epsilon, minibatch)
self.build_layer(num_nodes, validation, nodeCV_size, num_iter, alpha, epsilon)
pred_train = self.layer._predict(self.X_train)
pred_validate = self.layer._predict(self.X_validate_layer)
pred_test = self.layer._predict(X_test)
# stack training+validation sets, inverse transform, separate again
K = len(self.Y_train)
x_train = numpy.vstack((self.X_train, self.X_validate_layer))
y_train = numpy.hstack((self.Y_train, self.Y_validate_layer))
x_train = Postprocess(x_train, X_nottest_scaler)
y_train = Postprocess(y_train, Y_nottest_scaler)
pred_train = Postprocess(pred_train, Y_nottest_scaler)
pred_validate = Postprocess(pred_validate, Y_nottest_scaler)
pred_test = Postprocess(pred_test, Y_nottest_scaler)
self.X_train, self.X_validate_layer = [x_train[:K, :], x_train[K:, :]]
self.Y_train, self.Y_validate_layer = [y_train[:K], y_train[K:]]
self.layer.err_train = get_error(self.Y_train, pred_train)
self.layer.err_validate = get_error(self.Y_validate_layer, pred_validate)
self.layer.err_test = get_error(Y_test, pred_test)
self.layer.X_scaler = X_nottest_scaler
self.layer.Y_scaler = Y_nottest_scaler
print self.layer.err_train, self.layer.err_validate, self.layer.err_test
def init_layer(self, num_iter, alpha, epsilon, minibatch):
"""Initializes the layer and adds an initial node to it."""
self.layer = Layer()
node = OptimalNode(self.X_train_node, self.Y_train_node, bias=True,
num_iter=num_iter, alpha=alpha, minibatch=minibatch)
node.early_stop(self.X_validate_node, self.Y_validate_node)
node.lr = epsilon
node.is_useful(self.layer, self.X_validate_node, self.Y_validate_node)
self.layer.add_node(node)
node.train_err = get_error(self.Y_train_node,
self.layer._predict(self.X_train_node))
def build_layer(self, num_nodes, validation, nodeCV_size, num_iter, alpha, epsilon):
"""Builds a Layer by optimizing new nodes and adding them if they are useful.
Each successive node optimizes w.r.t. residuals of the previous iteration.
If the new node reduces error, the node is added to the layer. If it increases
error, it stops (unless a certain number of consecutive bad nodes are allowed)."""
for i in range(num_nodes):
if validation=='Shuffled':
train_validate = train_test_split(self.X_train, self.Y_train,
test_size=nodeCV_size)
[self.X_train_node, self.X_validate_node,
self.Y_train_node, self.Y_validate_node] = train_validate
Y_pseudo = self.Y_train_node-self.layer._predict(self.X_train_node)
Y_pseudo_validate = self.Y_validate_node-self.layer._predict(self.X_validate_node)
node = OptimalNode(self.X_train_node, Y_pseudo, bias=True,
num_iter=num_iter, alpha=alpha)
#.........这里部分代码省略.........