k = 5 indices = np.argsort(distances)[:k] classes = np.concatenate((np.zeros(10), np.ones(10), np.ones(10)*2), axis=0) pred_class = int(stats.mode(classes[indices])[0])

这段代码是一个 k-Nearest Neighbors (k-NN) 分类器的预测部分。k-NN 分类器是一种基于实例的学习方法，其基本思想是根据新样本与训练集中的样本之间的相似度来确定新样本的类别。

这段代码中，distances 是新样本与训练集中所有样本之间的距离，np.argsort(distances) 返回按距离排序后的索引数组，[:k] 取前 k 个最近邻样本的索引。classes 是训练集中每个样本对应的类别，这里假设有三类，用 0、1、2 表示。np.concatenate((np.zeros(10), np.ones(10), np.ones(10)*2), axis=0) 创建一个长度为 30 的一维数组，前 10 个元素是 0，中间 10 个元素是 1，后面 10 个元素是 2，表示三类样本在数组中的顺序。stats.mode(classes[indices]) 返回前 k 个最近邻样本的类别中出现次数最多的类别，即投票结果。int() 将类别转为整型，最终得到的 pred_class 就是新样本的预测类别。

需要注意的是，这段代码只是 k-NN 分类器的预测部分，需要在训练集上训练出一个模型，并根据具体应用场景选择合适的相似度度量方法和 k 值。

相关问题

Betelgeuse1 = cv2.imread('D:/360MoveData/Users/Norah/Desktop/chengpin/data prepare/validation/Betelgeuse1.jpg') image_array = np.asarray(Betelgeuse1) result_bet1 = find_M(image_array) train_data = np.concatenate((train_images_cas,train_images_ald,train_images_bet,train_images_cap)), axis=0) distances = np.sqrt(np.sum((train_data - result_bet1)**2, axis=1)) #然后，将距离从小到大排序，取前k个数据的类别作为imk的预测分类。可以使用以下代码实现： k = 5 indices = np.argsort(distances)[:k] classes = np.concatenate((np.zeros(10), np.ones(10), np.ones(10)*2), axis=0) pred_class = int(stats.mode(classes[indices])[0])

这段代码的功能是进行图像分类。首先，读取一张名为"Betelgeuse1.jpg"的图片，并将其转为numpy数组格式。然后，调用函数"find_M"对图片进行处理，得到一个特征向量"result_bet1"。接着，将三类训练数据（cas、ald、bet和cap）合并为一个数组"train_data"。通过计算测试图像"result_bet1"和每个训练图像的欧氏距离，得到一个距离数组"distances"。最后，将距离从小到大排序，并取前k个数据的类别作为"Betelgeuse1.jpg"的预测分类。其中，k的值为5，"indices"记录了距离最小的5个训练数据的索引，"classes"是训练数据的类别，"pred_class"是预测的类别，使用"mode"函数得到距离最小的5个训练数据中出现次数最多的类别作为预测分类。

umap.UMAP()函数源代码

以下是 `umap.UMAP()` 函数的源代码：

class UMAP(BaseEstimator, TransformerMixin):
    def __init__(
        self,
        n_neighbors=15,
        n_components=2,
        metric="euclidean",
        metric_kwds=None,
        output_metric="euclidean",
        output_metric_kwds=None,
        n_epochs=None,
        learning_rate=1.0,
        init="spectral",
        min_dist=0.1,
        spread=1.0,
        low_memory=False,
        set_op_mix_ratio=1.0,
        local_connectivity=1.0,
        repulsion_strength=1.0,
        negative_sample_rate=5,
        transform_queue_size=4.0,
        a=None,
        b=None,
        random_state=None,
        angular_rp_forest=False,
        target_n_neighbors=-1,
        target_metric="categorical",
        target_metric_kwds=None,
        target_weight=0.5,
        transform_seed=42,
        force_approximation_algorithm=False,
        verbose=False,
    ):
        self.n_neighbors = n_neighbors
        self.n_components = n_components
        self.metric = metric
        self.metric_kwds = metric_kwds
        self.output_metric = output_metric
        self.output_metric_kwds = output_metric_kwds
        self.n_epochs = n_epochs
        self.learning_rate = learning_rate
        self.init = init
        self.min_dist = min_dist
        self.spread = spread
        self.low_memory = low_memory
        self.set_op_mix_ratio = set_op_mix_ratio
        self.local_connectivity = local_connectivity
        self.repulsion_strength = repulsion_strength
        self.negative_sample_rate = negative_sample_rate
        self.transform_queue_size = transform_queue_size
        self.a = a
        self.b = b
        self.random_state = random_state
        self.angular_rp_forest = angular_rp_forest
        self.target_n_neighbors = target_n_neighbors
        self.target_metric = target_metric
        self.target_metric_kwds = target_metric_kwds
        self.target_weight = target_weight
        self.transform_seed = transform_seed
        self.force_approximation_algorithm = force_approximation_algorithm
        self.verbose = verbose

    def fit(self, X, y=None):
        self.fit_transform(X, y)
        return self

    def transform(self, X):
        if self.transform_mode_ == "embedding":
            if sparse.issparse(X):
                raise ValueError(
                    "Transform not available for sparse input in `" "transform_mode='embedding'`"
                )
            X = check_array(X, dtype=np.float32, accept_sparse="csr", order="C")
            X -= self._a
            X /= self._b
            return self._transform(X)
        elif self.transform_mode_ == "graph":
            if not sparse.issparse(X):
                raise ValueError(
                    "Transform not available for dense input in `" "transform_mode='graph'`"
                )
            return self.graph_transform(X)
        else:
            raise ValueError("Unknown transform mode '%s'" % self.transform_mode_)

    def fit_transform(self, X, y=None):
        if self.verbose:
            print(str(datetime.now()), "Start fitting UMAP...")
        self.fit_data = X

        if self.output_metric_kwds is None:
            self.output_metric_kwds = {}

        if self.metric_kwds is None:
            self.metric_kwds = {}

        if sparse.isspmatrix_csr(X) and _HAVE_PYNNDESCENT:
            self._sparse_data = True
            self._knn_index = make_nn_descent(
                self.fit_data,
                self.n_neighbors,
                self.metric,
                self.metric_kwds,
                self.angular_rp_forest,
                random_state=self.random_state,
                low_memory=self.low_memory,
                verbose=self.verbose,
            )
        else:
            self._sparse_data = False
            self._knn_index = make_nn_graph(
                X,
                n_neighbors=self.n_neighbors,
                algorithm="auto",
                metric=self.metric,
                metric_kwds=self.metric_kwds,
                angular=self.angular_rp_forest,
                random_state=self.random_state,
                verbose=self.verbose,
            )

        # Handle small cases efficiently by computing all distances
        if X.shape[0] < self.n_neighbors:
            self._raw_data = X
            self.embedding_ = np.zeros((X.shape[0], self.n_components))
            return self.embedding_

        if self.verbose:
            print(str(datetime.now()), "Construct fuzzy simplicial set...")
        self.graph_ = fuzzy_simplicial_set(
            X,
            self.n_neighbors,
            random_state=self.random_state,
            metric=self.metric,
            metric_kwds=self.metric_kwds,
            knn_indices=self._knn_index,
            angular=self.angular_rp_forest,
            set_op_mix_ratio=self.set_op_mix_ratio,
            local_connectivity=self.local_connectivity,
            verbose=self.verbose,
        )

        if self.verbose:
            print(str(datetime.now()), "Construct embedding...")
        self._raw_data = X

        if self.output_metric_kwds is None:
            self.output_metric_kwds = {}

        if self.target_n_neighbors == -1:
            self.target_n_neighbors = self.n_neighbors

        self.embedding_ = simplicial_set_embedding(
            self._raw_data,
            self.graph_,
            self.n_components,
            initial_alpha=self.learning_rate,
            a=self.a,
            b=self.b,
            gamma=1.0,
            negative_sample_rate=self.negative_sample_rate,
            n_epochs=self.n_epochs,
            init=self.init,
            spread=self.spread,
            min_dist=self.min_dist,
            set_op_mix_ratio=self.set_op_mix_ratio,
            local_connectivity=self.local_connectivity,
            repulsion_strength=self.repulsion_strength,
            metric=self.output_metric,
            metric_kwds=self.output_metric_kwds,
            verbose=self.verbose,
        )

        self.transform_mode_ = "embedding"

        return self.embedding_

    def graph_transform(self, X):
        if not sparse.issparse(X):
            raise ValueError(
                "Input must be a sparse matrix for transform with `transform_mode='graph'`"
            )
        if self.verbose:
            print(str(datetime.now()), "Transform graph...")
        if self._sparse_data:
            indices, indptr, data = _sparse_knn(self._knn_index, X.indices, X.indptr, X.data)
            indptr = np.concatenate((indptr, [indices.shape[0]]))
            knn_indices, knn_dists = indices, data
        else:
            knn_indices, knn_dists = query_pairs(
                self._knn_index,
                X,
                self.n_neighbors,
                return_distance=True,
                metric=self.metric,
                metric_kwds=self.metric_kwds,
                angular=self.angular_rp_forest,
                random_state=self.random_state,
                verbose=self.verbose,
            )
        graph = fuzzy_simplicial_set(
            X,
            self.n_neighbors,
            knn_indices=knn_indices,
            knn_dists=knn_dists,
            random_state=self.random_state,
            metric=self.metric,
            metric_kwds=self.metric_kwds,
            angular=self.angular_rp_forest,
            set_op_mix_ratio=self.set_op_mix_ratio,
            local_connectivity=self.local_connectivity,
            verbose=self.verbose,
        )
        self.transform_mode_ = "graph"
        return graph

    def _transform(self, X):
        if self.verbose:
            print(str(datetime.now()), "Transform embedding...")
        if self.transform_seed is None:
            self.transform_seed_ = np.zeros(self.embedding_.shape[1])
        else:
            self.transform_seed_ = self.embedding_[self.transform_seed, :].mean(axis=0)
        dists = pairwise_distances(
            X, Y=self.embedding_, metric=self.output_metric, **self.output_metric_kwds
        )
        rng_state = np.random.RandomState(self.transform_seed_)
        # TODO: make binary search optional
        adjusted_local_connectivity = max(self.local_connectivity - 1.0, 1e-12)
        inv_dist = 1.0 / dists
        inv_dist = make_heap(inv_dist)
        sigmas, rhos = smooth_knn_dist(
            inv_dist, self.n_neighbors, local_connectivity=adjusted_local_connectivity
        )
        rows, cols, vals = compute_membership_strengths(
            inv_dist, sigmas, rhos, self.negative_sample_rate, rng_state
        )
        graph = SparseGraph(
            X.shape[0],
            self.embedding_.shape[0],
            rows,
            cols,
            vals,
            self.transform_queue_size * X.shape[0],
            np.random.RandomState(self.transform_seed_),
            self.metric,
            self.output_metric_kwds,
            self.angular_rp_forest,
            self.verbose,
        )
        graph.compute_transition_matrix(self.repulsion_strength, self.epsilon)
        embedding = graph.compute_embedding(
            self.embedding_,
            self.learning_rate,
            self.n_epochs,
            self.min_dist,
            self.spread,
            self.init,
            self.set_op_mix_ratio,
            self._a,
            self._b,
            self.gamma,
            self.rp_tree_init,
            self.rp_tree_init_eps,
            self.metric,
            self.output_metric_kwds,
            self.random_state,
            self.verbose,
        )
        return embedding

    def set_op_mix_ratio(self, mix_ratio):
        self.set_op_mix_ratio = mix_ratio

    def fuzzy_simplicial_set(
        X,
        n_neighbors,
        metric="euclidean",
        metric_kwds=None,
        random_state=None,
        knn_indices=None,
        angular=False,
        set_op_mix_ratio=1.0,
        local_connectivity=1.0,
        verbose=False,
    ):
        return fuzzy_simplicial_set(
            X,
            n_neighbors,
            metric=metric,
            metric_kwds=metric_kwds,
            random_state=random_state,
            knn_indices=knn_indices,
            angular=angular,
            set_op_mix_ratio=set_op_mix_ratio,
            local_connectivity=local_connectivity,
            verbose=verbose,
        )

    def simplicial_set_embedding(
        data,
        graph,
        n_components,
        initial_alpha=1.0,
        a=None,
        b=None,
        gamma=1.0,
        negative_sample_rate=5,
        n_epochs=None,
        init="spectral",
        spread=1.0,
        min_dist=0.1,
        set_op_mix_ratio=1.0,
        local_connectivity=1.0,
        repulsion_strength=1.0,
        metric="euclidean",
        metric_kwds=None,
        verbose=False,
    ):
        return simplicial_set_embedding(
            data,
            graph,
            n_components,
            initial_alpha=initial_alpha,
            a=a,
            b=b,
            gamma=gamma,
            negative_sample_rate=negative_sample_rate,
            n_epochs=n_epochs,
            init=init,
            spread=spread,
            min_dist=min_dist,
            set_op_mix_ratio=set_op_mix_ratio,
            local_connectivity=local_connectivity,
            repulsion_strength=repulsion_strength,
            metric=metric,
            metric_kwds=metric_kwds,
            verbose=verbose,
        )

该函数实现了UMAP算法，是非常复杂的代码。简单来说，它实现了以下步骤：

- 初始化UMAP对象的各种参数。
- 根据输入数据计算k近邻图，这一步可以使用pyNNDescent或BallTree算法。
- 构建模糊单纯形集，用于表示原始数据的流形结构。
- 计算新的嵌入空间，用于将原始数据降维到低维空间。
- 支持transform方法，以便在已经学习了嵌入空间之后将新的数据映射到该空间中。
- 支持fuzzy_simplicial_set和simplicial_set_embedding方法，以便使用UMAP算法的不同组件。