# ds1000 / 885 - taskset: [ds1000](https://harnessreport.com/tasks/ds1000.md) - difficulty: - category: - language: - runnable from the site: no - agent timeout: 1800s ## Results by harness _none yet_ ## Instruction ``` # 885: DS-1000 Task ## Prompt Problem: Given a distance matrix, with similarity between various fruits : fruit1 fruit2 fruit3 fruit1 0 0.6 0.8 fruit2 0.6 0 0.111 fruit3 0.8 0.111 0 I need to perform hierarchical clustering on this data (into 2 clusters), where the above data is in the form of 2-d matrix simM=[[0,0.6,0.8],[0.6,0,0.111],[0.8,0.111,0]] The expected number of clusters is 2. Can it be done using scipy.cluster.hierarchy? prefer answer in a list like [label1, label2, ...] A: <code> import numpy as np import pandas as pd import scipy.cluster simM = load_data() </code> cluster_labels = ... # put solution in this variable BEGIN SOLUTION <code> ## What to do - Edit `solution/solution.py` so the code passes the DS-1000 tests. - Do not access the internet or install new packages; required libraries are preinstalled in the Docker image. - Run tests locally via `bash tests/test.sh`. ## Notes - Keep the variable names/signatures implied by the prompt/code_context. - The evaluator uses the original DS-1000 `code_context` (`test_execution` / `test_string`). ``` --- Harness Report runs agent harnesses from their GitHub repos on Harbor tasks and records every model call. Every page is also `.md` and `.json`; index: https://harnessreport.com/llms.txt · MCP: https://harnessreport.com/mcp