Asked  6 Months ago    Answers:  4   Viewed   30 times

This Python code:

import numpy as p

def firstfunction():
    UnFilteredDuringExSummaryOfMeansArray = []
    dataMatrix = BeatByBeatMatrixOfMatrices[column]
    roughTrimmedMatrix = p.array(dataMatrix[1:,1:17])

    trimmedMatrix = p.array(roughTrimmedMatrix,dtype=p.float64)  #ERROR THROWN HERE

    myMeans = p.mean(trimmedMatrix,axis=0,dtype=p.float64)
    conditionMeansArray = [TestID,testCondition,'UnfilteredBefore',myMeans[3], myMeans[4], 
                           myMeans[6], myMeans[9], myMeans[10], myMeans[11], myMeans[12],
                           myMeans[13], myMeans[14], myMeans[15]]

def secondfunction(UnFilteredDuringExSummaryOfMeansArray):
    RRDuringArray = p.array(UnFilteredDuringExSummaryOfMeansArray,dtype=p.float64)[1:,3]


Throws this error message:

File "", line 3484, in secondfunction
RRDuringArray = p.array(UnFilteredDuringExSummaryOfMeansArray,dtype=p.float64)[1:,3]
ValueError: setting an array element with a sequence.

Can anyone show me what to do to fix the problem in the broken code above so that it stops throwing an error message?

EDIT: I did a print command to get the contents of the matrix, and this is what it printed out:

UnFilteredDuringExSummaryOfMeansArray is:

[['TestID', 'ConditionName', 'FilterType', 'RRMean', 'HRMean', 'dZdtMaxVoltageMean', 'BZMean', 'ZXMean', 'LVETMean', 'Z0Mean', 'StrokeVolumeMean', 'CardiacOutputMean', 'VelocityIndexMean'],
[u'HF101710', 'PreEx10SecondsBEFORE', 'UnfilteredBefore', 0.90670000000000006, 66.257731979420001, 1.8305673000000002, 0.11750000000000001, 0.15120546389880002, 0.26870546389879996, 27.628261216480002, 86.944190346160013, 5.767261352345999, 0.066259118585869997],
[u'HF101710', '25W10SecondsBEFORE', 'UnfilteredBefore', 0.68478571428571422, 87.727887206978565, 2.2965444125714285, 0.099642857142857144, 0.14952476549885715, 0.24916762264164286, 27.010483303721429, 103.5237336525, 9.0682762747642869, 0.085022572648242867],
[u'HF101710', '50W10SecondsBEFORE', 'UnfilteredBefore', 0.54188235294117659, 110.74841107829413, 2.6719262705882354, 0.077705882352917643, 0.15051306356552943, 0.2282189459185294, 26.768787504858825, 111.22827075238826, 12.329456404418824, 0.099814258468417641],
[u'HF101710', '75W10SecondsBEFORE', 'UnfilteredBefore', 0.4561904761904762, 131.52996981880955, 3.1818159523809522, 0.074714285714290493, 0.13459344175047619, 0.20930772746485715, 26.391156337028569, 123.27387909873812, 16.214243779323812, 0.1205685359981619]]

Looks like a 5 row by 13 column matrix to me, though the number of rows is variable when different data are run through the script. With this same data that I am adding in this.

EDIT 2: However, the script is throwing an error. So I do not think that your idea explains the problem that is happening here. Thank you, though. Any other ideas?


FYI, if I replace this problem line of code:

    RRDuringArray = p.array(UnFilteredDuringExSummaryOfMeansArray,dtype=p.float64)[1:,3]

with this instead:

    RRDuringArray = p.array(UnFilteredDuringExSummaryOfMeansArray)[1:,3]

Then that section of the script works fine without throwing an error, but then this line of code further down the line:


Throws this error:

File "", line 3631, in CreateSummaryGraphics
TypeError: cannot perform reduce with flexible type

So you can see that I need to specify the data type in order to be able to use ylim in matplotlib, but yet specifying the data type is throwing the error message that initiated this post.



From the code you showed us, the only thing we can tell is that you are trying to create an array from a list that isn't shaped like a multi-dimensional array. For example

numpy.array([[1,2], [2, 3, 4]])


numpy.array([[1,2], [2, [3, 4]]])

will yield this error message, because the shape of the input list isn't a (generalised) "box" that can be turned into a multidimensional array. So probably UnFilteredDuringExSummaryOfMeansArray contains sequences of different lengths.

Edit: Another possible cause for this error message is trying to use a string as an element in an array of type float:

numpy.array([1.2, "abc"], dtype=float)

That is what you are trying according to your edit. If you really want to have a NumPy array containing both strings and floats, you could use the dtype object, which enables the array to hold arbitrary Python objects:

numpy.array([1.2, "abc"], dtype=object)

Without knowing what your code shall accomplish, I can't judge if this is what you want.

Tuesday, June 1, 2021
answered 6 Months ago

Finally I found the answer to my question with the help of some ideas from @larsmans and @eickenberg. The problem was that X_train did not have the same number of elements in all the arrays. So, it was not able to form a 2D array. Now that I have added an additional value to that array, the dimensionality matched for all the 1D arrays and X_train was able to form a 2D array. Thanks for the ideas guys!

Saturday, July 3, 2021
answered 5 Months ago

Based on the exception you get it seems likely that temp is an object array containing sequences. You could simply use numpy.empty_like:

data = np.empty_like(temp)  # instead of "data = np.empty(temp.shape)"

This creates a new empty array with the same shape and dtype - like your original array.

For example:

import numpy as np

temp = np.empty((181, 360), dtype=object)
for i in range(maxlat) :
    for j in range(maxlon):
        temp[i][j] = [1, 2, 3]

With the new approach it works:

data = np.empty_like(temp)
maxlat = temp.shape[0]
maxlon = temp.shape[1]
print(maxlat, maxlon)

for i in range(maxlat) :
    for j in range(maxlon):
        data[i][j] = temp[i][j]

And this temp array also reproduces the exception on your original code sample:

data = np.empty(temp.shape)  # your approach
maxlat = temp.shape[0]
maxlon = temp.shape[1]
print(maxlat, maxlon)

for i in range(maxlat) :
    for j in range(maxlon):
        data[i][j] = temp[i][j]

throws the exception:

ValueError: setting an array element with a sequence.

Wednesday, November 17, 2021
answered 2 Weeks ago

Most (if not all) scikit-learn functions expect as input X, a 2D array with shape (n_samples, n_features).

See the doc:

Fit Gaussian Naive Bayes according to X, y

Parameters: X : array-like, shape (n_samples, n_features)

Training vectors, where n_samples is the number of samples and n_features is the number of features.

To solve your problem, use a vector representation of the images and then put each vector as a row in your x_train matrix.

Finally, use this X for the fitting of the GaussianNB.

How to vectorize an image ?

Use something like this:

import numpy as np
from PIL import Image

img ='orig.png').convert('RGBA')
arr = np.array(img)

# make a 1-dimensional view of arr
flat_arr = arr.ravel()
Saturday, November 27, 2021
answered 2 Days ago
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