Cannot find appropriate system libraries for WITH_SSL=system. Make sure you have specified a supported SSL version. Valid options are : system (use the OS openssl library), yes (synonym for system), </path/to/custom/openssl/installation> CMake Error at cmake/ssl.cmake:66 (MESSAGE): Please install the appropriate openssl developer package. Call Stack (most recent call first): cmake/ssl.cmake:260 (FATAL_SSL_NOT_FOUND_ERROR) CMakeLists.txt:483 (MYSQL_CHECK_SSL)

时间: 2024-02-16 13:02:34 浏览: 383
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sars_rangemigration_system6dj_wk_wk插值_appropriate32t_

这个错误是由于缺少 OpenSSL 库或版本不支持导致的。解决方法如下： 1. 确保 OpenSSL 库已经安装。在 Linux 上，您可以使用包管理器安装 OpenSSL 库，例如 apt-get、yum 等。在 Windows 上，您可以从 OpenSSL 官网下载适合您的版本并安装。 2. 如果您已经安装了 OpenSSL 库，但是仍然遇到此错误，请确保您使用的是支持的版本。MySQL 可以使用 OpenSSL 1.0.x 或 1.1.x 版本。检查您的 OpenSSL 版本并确保它与 MySQL 兼容。 3. 如果您使用的是自定义 OpenSSL 安装路径，请在 WITH_SSL 选项中指定路径，例如：-DWITH_SSL=/path/to/openssl。 4. 如果您在构建 MySQL 时使用了系统 OpenSSL 库，并且仍然遇到此错误，请确保您的系统 OpenSSL 库版本与 MySQL 兼容。您可以尝试使用自定义 OpenSSL 安装路径或升级您的系统 OpenSSL 库。 如果您仍然无法解决此问题，请提供更多上下文信息，例如您正在尝试构建什么，使用的操作系统和 CMake 版本等。
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class GetKmers: def __init__(self, out_dir, kmer, job_id, chr_dir, chromsize_path, idx_path): """Creates all the possible k-mers for part of the genome. Used a referece file to find the appropriate chromosome, start and end position. Passes through the fasta file of the chromosome and generates all of the possible k-mers. Args: :param out_dir: Directory for saving <chrom>.<jobid>.kmer.gz files :param str kmer: k-mer string such as 'k24' :param int job_id: Reference ID used for finding chrom, start and end :param chr_dir: Path to directory with chromosome fasta files :param chromsize_path: Path to 2 column file of chrom\tsize\n :param idx_path: Path to 4 column file of index\tchrom\tst\tend\n :returns: An object with methods such as get_step_fasta(), get_seq_ar(), write_kmers() and write_regions(). :raises ValueError: if expected chromosome path does not exist """ self.out_dir = out_dir self.kmer = kmer self.job_id = job_id self.chromsize_path = chromsize_path self.chr_dir = chr_dir self.idx_path = idx_path self.chrom, self.start, self.end = self.get_region() self.chrom_path = "{}/{}.fasta".format( self.chr_dir, self.chrom) if not os.path.exists(self.chrom_path): raise ValueError( "{} does not exist".format(self.chrom_path)) elif not os.path.exists(self.idx_path): raise ValueError( "{} does not exist".format(self.idx_path))分析这段代码的作用

这段代码定义了一个名为 GetKmers 的类，用于创建基因组局部区域的所有可能的 k-mer，通过参考文件查找适当的染色体，开始和结束位置。该类具有获取步骤 fasta 文件、获取序列数组、写入 k-mer 和写入区域等方法。...

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Task: - Download the UCM dataset from the official source or any reputable repository. - Preprocess the dataset by splitting it into training, validation, and test sets. Please use an 80-10-10 split, respectively. - Choose a suitable deep-learning algorithm for scene classification. You can use popular classifiers such as Convolutional Neural Networks (CNNs). - Implement the chosen algorithm in Python, using appropriate libraries. - Train the classifier using the training set and tune the hyperparameters using the validation set. - Evaluate the trained classifier on the test set using the following metrics: Accuracy: The overall classification accuracy. Precision: The ability to correctly classify positive samples. Recall: The ability to correctly identify positive samples. F1-score: The harmonic mean of precision and recall. Confusion Matrix: A table showing the number of correct and incorrect predictions for each class. Report: Prepare a one-page report summarizing the results of your scene classification experiment and the architecture of your network. Submit your Python code along with the one-page report summarizing the results. Make sure to organize your code and report in a clear and readable manner.

Sure, here's an outline of the steps you'll need to take to complete this task: 1. Download the UCM dataset from the official source or any reputable repository. 2. Preprocess the dataset by ...

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