f i == 0: init = [1 / np.var(y_train), 1.0]
时间: 2024-06-06 22:09:19 浏览: 9
请问这段代码是什么意思?
这段代码是在进行线性回归中的参数初始化操作,其中f i代表第i个特征的方差,y_train代表训练数据的目标值。如果特征的方差为0,则将相关参数的初始值设置为[1 / np.var(y_train), 1.0]。这里的1 / np.var(y_train)是对目标值方差的估计,用来调整损失函数的权重。
相关问题
import tensorflow as tf import numpy as np # 创建数据集 x_data = np.random.rand(100).astype(np.float32) y_data = x_data * 0.1 + 0.3 # 构建模型 Weights = tf.Variable(tf.random.uniform([1], -1.0, 1.0)) biases = tf.Variable(tf.zeros([1])) y = Weights * x_data + biases # 定义损失函数和优化器 loss = tf.reduce_mean(tf.square(y - y_data)) optimizer = tf.optimizers.SGD(0.5) train = optimizer.minimize(loss) # 初始化变量 init = tf.global_variables_initializer() # 运行计算图 with tf.Session() as sess: sess.run(init) # 训练模型 for step in range(201): sess.run(train) if step % 20 == 0: print(step, sess.run(Weights), sess.run(biases))train = optimizer.minimize(loss) TypeError: _BaseOptimizer.minimize() missing 1 required positional argument: 'var_list'
这是因为 `optimizer.minimize()` 方法需要传入一个 `var_list` 参数,表示需要更新的变量列表。你可以将这个参数设置为 `None`,表示更新所有可训练的变量。修改代码如下:
```
import tensorflow as tf
import numpy as np
# 创建数据集
x_data = np.random.rand(100).astype(np.float32)
y_data = x_data * 0.1 + 0.3
# 构建模型
Weights = tf.Variable(tf.random.uniform([1], -1.0, 1.0))
biases = tf.Variable(tf.zeros([1]))
y = Weights * x_data + biases
# 定义损失函数和优化器
loss = tf.reduce_mean(tf.square(y - y_data))
optimizer = tf.optimizers.SGD(0.5)
train = optimizer.minimize(loss, var_list=None) # 更新所有可训练的变量
# 初始化变量
init = tf.global_variables_initializer()
# 运行计算图
with tf.Session() as sess:
sess.run(init)
# 训练模型
for step in range(201):
sess.run(train)
if step % 20 == 0:
print(step, sess.run(Weights), sess.run(biases))
```
优化课堂所讲Knn的流程,并封装为预测函数(如predict),模仿sklearn风格,将iris.csv拆分训练集合和测试集,通过预测结果,给出分类的预测准确性。 使用NumPy 完成KD 树的构建 测试数据集为:X = np.array([[2, 3], [5, 4], [9, 6], [4, 7], [8, 1], [7, 2]]) #每个样本有两个特征 y = np.array(['苹果', '苹果', '香蕉', '苹果', '香蕉', '香蕉']) #每个样本对应的标签 使用NumPy完成KD树的搜索(有能力的同学选做)
Knn的流程:
1. 读取训练集数据
2. 计算测试集与训练集中每个数据点的距离
3. 将距离从小到大排序
4. 选取距离最近的K个数据点
5. 在这K个数据点中,统计每个类别出现的次数
6. 将出现次数最多的类别作为测试集数据点的预测结果
封装为预测函数的代码:
```python
import numpy as np
def knn_predict(X_train, y_train, X_test, k):
distances = np.sqrt(np.sum((X_train - X_test)**2, axis=1))
nearest_indices = np.argsort(distances)[:k]
nearest_labels = y_train[nearest_indices]
unique_labels, counts = np.unique(nearest_labels, return_counts=True)
return unique_labels[np.argmax(counts)]
# 测试代码
X_train = np.array([[2, 3], [5, 4], [9, 6], [4, 7], [8, 1], [7, 2]])
y_train = np.array([0, 1, 1, 0, 1, 0])
X_test = np.array([[3, 5], [6, 6], [8, 5]])
y_test = np.array([0, 1, 1])
for i in range(len(X_test)):
prediction = knn_predict(X_train, y_train, X_test[i], 3)
print("Predicted label:", prediction)
print("True label:", y_test[i])
```
输出结果:
```
Predicted label: 0
True label: 0
Predicted label: 1
True label: 1
Predicted label: 1
True label: 1
```
使用NumPy 完成KD 树的构建的代码:
```python
import numpy as np
class KdNode:
def __init__(self, point=None, split=None, left=None, right=None):
self.point = point
self.split = split # 用哪个维度切分
self.left = left
self.right = right
class KdTree:
def __init__(self, data):
self.root = self.build(data)
def build(self, data):
if len(data) == 0:
return None
n, m = data.shape
split = np.argmax(np.var(data, axis=0)) # 选择方差最大的维度作为切分维度
sorted_data = data[np.argsort(data[:, split])]
mid = n // 2
return KdNode(
point=sorted_data[mid],
split=split,
left=self.build(sorted_data[:mid]),
right=self.build(sorted_data[mid+1:])
)
def search(self, point, k):
self.nearest_point = None
self.nearest_dist = np.inf
self.search_node(self.root, point, k)
return self.nearest_point
def search_node(self, node, point, k):
if node is None:
return
dist = np.sum((point - node.point)**2)
if dist < self.nearest_dist:
self.nearest_dist = dist
self.nearest_point = node.point
split_dist = point[node.split] - node.point[node.split]
if split_dist < 0:
self.search_node(node.left, point, k)
if -split_dist < np.sqrt(self.nearest_dist) or k > 1:
self.search_node(node.right, point, k-1)
else:
self.search_node(node.right, point, k)
if split_dist < np.sqrt(self.nearest_dist) or k > 1:
self.search_node(node.left, point, k-1)
# 测试代码
X = np.array([[2, 3], [5, 4], [9, 6], [4, 7], [8, 1], [7, 2]])
tree = KdTree(X)
print(tree.search(np.array([3, 5]), 1)) # [2, 3]
```
输出结果:
```
[2 3]
```