Tag: Stackify

The earliest available version on windows is 0.4.0 As can be seen when searching for the available version on conda. $conda search pytorch-cpu -c pytorch Loading channels: done # Name Version Build Channel pytorch-cpu 0.4.0 py35_cpuhe774522_1 pytorch pytorch-cpu 0.4.0 py36_cpuhe774522_1 pytorch pytorch-cpu 0.4.1 py35_cpuhe774522_1 pytorch pytorch-cpu 0.4.1 py36_cpuhe774522_1 pytorch pytorch-cpu…

You need to define target output files using rule all. SAMPLES = [‘1’, ‘2’, ‘3’, ‘4’] rule all: input: expand(“sample{sample}.R{read_no}.fq.gz.out”, sample=SAMPLES, read_no=[‘1’, ‘2’]) rule fastp: input: reads1=”sample{sample}.R1.fq.gz”, reads2=”sample{sample}.R2.fq.gz” output: reads1out=”sample{sample}.R1.fq.gz.out”, reads2out=”sample{sample}.R2.fq.gz.out” shell: “fastp -i {input.reads1} -I {input.reads2} -o {output.reads1out} -O {output.reads2out}” This is the output of command snakemake -np, with…

It looks like the model hyper parameters are not rightly tuned. It is difficult to make conclusions with so little data but if you use: model = MultinomialNB(0.5).fit(X, y) # or model = LogisticRegression().fit(X, y) you will get the expected results, at least for words like “Greenpeace”, “Obama”, “President” which…

It is most likely correct. PyTorch only sees two GPUs (therefore indexed 0 and 1) which are actually your GPU 5 and 6. Check the actual usage with nvidia-smi. If it is still inconsistent, you might need to set an environment variable: export CUDA_DEVICE_ORDER=PCI_BUS_ID (See Inconsistency of IDs between ‘nvidia-smi…

SQLite is a file level database, hence to reference it requires a full directory path. No where do you specify the working directory or a full path in the file name. By default, R will use the current working directory contained in getwd(). If database is not contained in this…

Just had this problem and found a solution. When searching (with Bing or Google) for conda install of pymc3, several links come up. The first is with conda-forge: conda install -c conda-forge pymc3 DO NOT USE THIS or you will get the error messages in the above posts. I have…

The correct place for this question is on the Bioconductor support site. It’s better to provide a reproducible example that captures the essence of the problem; creating the reproducible example often helps to identify the reason for the problem. library(Biobase) exprs <- matrix(0, nrow=5, ncol=3, dimnames=list(letters[1:5], LETTERS[1:3])) pData <- data.frame(id=c(“foo”,…

You can use pipe: tmp_df = df. drop(“Gene type”, axis=1). rename(columns = { “Gene stable ID”: “ENSG”, “Gene name”: “gene_name”, “miRBase accession”: “MI”, “miRBase ID”: “mirna_name” }). pipe(lambda x: x.assign(species = x.mirna_name.str[:3])) tmp_df Out[365]: ENSG gene_name MI mirna_name species 0 ENSG00000274494 MIR6832 MI0022677 hsa-mir-6832 hsa 1 ENSG00000283386 MIR4659B MI0017291 hsa-mir-4659b…