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BigCodeBench / BigCodeBench v0.1.0_hf 23265cc6-cef1-5253-b2c0-17a698fb9bfb

Problem

Answer published by the source. Consult the official source to check your work against its answer.

complete prompt

import numpy as np
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt


def task_func(P, T, tensor_shape=(3, 3, 3)):
    """
    Calculate the product of a matrix "P" and a 3D tensor "T" with numpy and then apply PCA to reduce the
    dimensionality of the result. The resulting 2D data is then visualized.
    Note: This function only accepts numpy matrices/arrays.

    Parameters:
    P (numpy.ndarray): The input matrix.
    T (numpy.ndarray): The input tensor. Must have same shape as tensor_shape.
    tensor_shape (tuple, optional): The shape of the tensor. Must be same as T.shape. Default is (3, 3, 3).

    Returns:
    pca_result (numpy.ndarray): The result of PCA of shape (N, 2), where N is the number of rows in matrix P.
    ax (matplotlib.axes.Axes): Plot of 'PCA Result Visualization', with 'Principal Component 1' on the x-axis
                               and 'Principal Component 2' on the y-axis.



    Requirements:
    - numpy
    - sklearn.decomposition
    - matplotlib.pyplot

    Example:
    >>> P = np.array([[6, 2, 7], [1, 1, 8], [8, 7, 1], [9, 6, 4], [2, 1, 1]])
    >>> T = np.array([[[1, 2, 3], [4, 5, 6], [7, 8, 9]], [[1, 2, 3], [4, 5, 6], [7, 8, 9]], [[1, 2, 3], [4, 5, 6], [7, 8, 9]]])
    >>> pca_result, ax = task_func(P, T)
    >>> pca_result.shape
    (3, 2)
    >>> type(ax)
    <class 'matplotlib.axes._axes.Axes'>
    """

instruct prompt

Calculate the product of a matrix "P" and a 3D tensor "T" with numpy and then apply PCA to reduce the dimensionality of the result. The resulting 2D data is then visualized.
Note that: This function only accepts numpy matrices/arrays.
The function should output with:
    pca_result (numpy.ndarray): The result of PCA of shape (N, 2), where N is the number of rows in matrix P.
    ax (matplotlib.axes.Axes): Plot of 'PCA Result Visualization', with 'Principal Component 1' on the x-axis
    and 'Principal Component 2' on the y-axis.
You should write self-contained code starting with:

Code

import numpy as np
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
def task_func(P, T, tensor_shape=(3, 3, 3)):

code prompt

Code

import numpy as np
from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
def task_func(P, T, tensor_shape=(3, 3, 3)):

entry point

task_func

libs

  • numpy
  • matplotlib
  • sklearn

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Source and history

task_id
23265cc6-cef1-5253-b2c0-17a698fb9bfb
task_key
default--v0~2e1~2e0~5fhf--23265cc6-cef1-5253-b2c0-17a698fb9bfb
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import