y : :py:class:`ndarray <numpy.ndarray>` of shape `(N, \*)` or None An array of target values / labels associated with the entries in `X`. Default is None. """ # 分割数据集,得到中心点、左子集、右子集 centroid, left_X, left_y, right_X, right_y = self._split(X, y) # ...
unk, filter_stopwords, filter_punctuation) # 设置模型的超参数 id 为 "AdditiveNGram" self.hyperparameters["id"] = "AdditiveNGram" # 设置模型的超参数 K 为传入的参数 K self.hyperparameters["K"] = K def log_prob(self, words, N): r""" Compute the smoothed log probability...
Create a filter array that will return only values higher than 42: import numpy as nparr = np.array([41, 42, 43, 44])# Create an empty listfilter_arr = []# go through each element in arrfor element in arr: # if the element is higher than 42, set the value to True, otherwise...
array([1, 7, 2, 5, 3, 9, 9, 4]), array([1, 4, 3, 8, 8, 0, 4])]
Python code to filter integers in NumPy float array# Import numpy import numpy as np # Creating an array arr = np.array([0.0, 0.01, 1.0, 2.0, 2.001, 2.002]) # Display array print("Original array:\n",arr,"\n") # Filtering out integer values res = arr[arr == arr.astype(int)] ...
An array of target values / labels associated with the entries in `X`. Default is None. """# 分割数据集,得到中心点、左子集、右子集centroid, left_X, left_y, right_X, right_y = self._split(X, y)# 创建根节点,设置中心点和半径self.root = BallTreeNode(centroid=centroid) ...
Freeze the base layer's parameters at their current values so they can no longer be updated. """# 冻结基础层的参数,使其无法再更新self._base_layer.freeze()defunfreeze(self):"""Unfreeze the base layer's parameters so they can be updated."""# 解冻基础层的参数,使其可以更新self._base_lay...
Suppose that we are given a NumPy array that contains some integer values and we need to create a mask that masks elements with values that lie between a certain range.Performing element-wise Boolean operations on NumPy arraysFor this purpose, we will use the simple OR operation which will...
filtered = np.array(filtered) print(f"filtered: {filtered}") Output: data: [[0 1] [2 3] [4 5] [6 7] [8 9] [0 1] [2 3] [4 5] [6 7] [8 9]] valid_indices: [(6, 7), (0, 1)] filtered: [[0 1] [6 7] ...
Here, we havex, an array of 4 values, and we have implicitly multiplied every element inxby 2, a single value. y=np.array([0,1,2,1])print(x+y) [1 3 5 5] Now, we have added together each element inxto its corresponding element iny, an array of the same shape. ...