I am new to this forum. Let me get started: I work on MATLAB and keep getting errors all the time. Finally i found a good forum like yours. My problem is this: I have an image which I want to put inside a large matrix. Everytime I do it I get
??? ERROR: subscripted assignment dimension mismatch
I tried everything possible, like u say resize, repmat, reshape....but I could not guess what is going wrong.
My code is like this:
nem(:,:,1) = image %// <-- error subscripted assignment dimension mismatch
my size of image is
71 * 71
bytes :----40328
class :----double
nem is created by
nem = zeros([size(inputimage,1),size(inputimage,2),12]);
size of inputmage is
[m,n,o] = size(inputimage);
m = 584 n = 565 o = 1
and size of nem:
[m,n,o] = size(img_out);
m = 584 n = 565 o = 12
You are trying to "fit" image a 71-by-71 matrix into mem(:,:,1) which is 584-by-565 matrix.
How do you expect Matlab to do this type of assignment??
You can fit image into a part of mem
>> mem( 1:size(image,1), 1:size(image,2), 1 ) = image
Related
I am getting error mentioned in the title and didn't find yet a solution.
X = train[feats].values
y = train['Target'].values
cv = StratifiedKFold(n_splits=3, random_state=2021, shuffle=True)
model = LogisticRegression(solver='liblinear')
scores = []
for train_idx, test_idx in cv.split(X, y):
model.fit(X[train_idx], y[train_idx])
y_pred = model.predict(X[test_idx])
score = mean_absolute_error(y[test_idx], y_pred )
scores.append(score)
print(np.mean(scores), np.std(scores))
fig = plt.figure(figsize=(15,6));
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
skplt.metrics.plot_confusion_matrix(y, y_pred, ax = ax1) #error line
skplt.metrics.plot_roc(y, y_pred, ax = ax2)
ValueError: Found input variables with inconsistent numbers of samples: [32561, 10853]
I checked the code, read many threads on this error. Somebody suggested me as a solution to put the cross-validation in a loop, but I don't know how to manage this with code (and also which part of operation to put in a loop, and how to write a condition that should be ending this loop). Please, help me with a specific answer that will help me to easily fix problem with my current level of advancement.
I have the same question from this topic:
How to get the correlation matrix of a pyspark data frame?
"I have a big pyspark data frame. I want to get its correlation matrix. I know how to get it with a pandas data frame.But my data is too big to convert to pandas. So I need to get the result with pyspark data frame.I searched other similar questions, the answers don't work for me. Can any body help me? Thanks!"
df4 is my dataset, he has 9 columns and all of them are integers:
reference__YM_unix:integer
tenure_band:integer
cei_global_band:integer
x_band:integer
y_band:integer
limit_band:integer
spend_band:integer
transactions_band:integer
spend_total:integer
I have first done this step:
# convert to vector column first
vector_col = "corr_features"
assembler = VectorAssembler(inputCols=df4.columns, outputCol=vector_col)
df_vector = assembler.transform(df4).select(vector_col)
# get correlation matrix
matrix = Correlation.corr(df_vector, vector_col)
And got the following output:
(matrix.collect()[0]["pearson({})".format(vector_col)].values)
Out[33]: array([ 1. , 0.0760092 , 0.09051543, 0.07550633, -0.08058203,
-0.24106848, 0.08229602, -0.02975856, -0.03108094, 0.0760092 ,
1. , 0.14792512, -0.10744735, 0.29481762, -0.04490072,
-0.27454922, 0.23242408, 0.32051685, 0.09051543, 0.14792512,
1. , -0.03708623, 0.13719527, -0.01135489, 0.08706559,
0.24713638, 0.37453265, 0.07550633, -0.10744735, -0.03708623,
1. , -0.49640664, 0.01885793, 0.25877516, -0.05019079,
-0.13878844, -0.08058203, 0.29481762, 0.13719527, -0.49640664,
1. , 0.01080777, -0.42319841, 0.01229877, 0.16440178,
-0.24106848, -0.04490072, -0.01135489, 0.01885793, 0.01080777,
1. , 0.00523737, 0.01244241, 0.01811365, 0.08229602,
-0.27454922, 0.08706559, 0.25877516, -0.42319841, 0.00523737,
1. , 0.32888075, 0.21416322, -0.02975856, 0.23242408,
0.24713638, -0.05019079, 0.01229877, 0.01244241, 0.32888075,
1. , 0.53310864, -0.03108094, 0.32051685, 0.37453265,
-0.13878844, 0.16440178, 0.01811365, 0.21416322, 0.53310864,
1. ])
I've tried to insert this result on arrays or an excel file but it didnt work.
I did:
matrix2 = (matrix.collect()[0]["pearson({})".format(vector_col)])
Then I got the following error when I tried to display this info:
display(matrix2)
Exception: ML model display does not yet support model type <class 'pyspark.ml.linalg.DenseMatrix'>.
I was expecting to insert the name of the columns back from df4 but it didnt succeed, I've read that I need to use df4.columns but I have no idea how does it works.
Finally, I was expecting to print the following graph that I've seen from medium article
https://medium.com/towards-artificial-intelligence/feature-selection-and-dimensionality-reduction-using-covariance-matrix-plot-b4c7498abd07
But also it didn't work:
from sklearn.preprocessing import StandardScaler
stdsc = StandardScaler()
X_std = stdsc.fit_transform(df4.iloc[:,range(0,7)].values)
cov_mat =np.cov(X_std.T)
plt.figure(figsize=(10,10))
sns.set(font_scale=1.5)
hm = sns.heatmap(cov_mat,
cbar=True,
annot=True,
square=True,
fmt='.2f',
annot_kws={'size': 12},
cmap='coolwarm',
yticklabels=cols,
xticklabels=cols)
plt.title('Covariance matrix showing correlation coefficients', size = 18)
plt.tight_layout()
plt.show()
AttributeError: 'DataFrame' object has no attribute 'iloc'
I've tried to replace df4 to matrix2 and didn't work too
You can use the following to get the correlation matrix in a form you can manipulate:
matrix = matrix.toArray().tolist()
From there you can convert to a dataframe pd.DataFrame(matrix) which would allow you to plot the heatmap, or save to excel etc.
A unique question I guess, given these unciode block elements:
https://en.wikipedia.org/wiki/Block_Elements
I want to get the relevant block element based on the matrix I get, so
11
01 will give ▜
00
10 will give ▖
and so on
I managed to do this in python, but I wonder if anyone got a more elegant solution.
from itertools import product
elements = [0, 1]
a = product(elements, repeat=2)
b = product(a, repeat=2)
matrices = [c for c in b]
"""
Matrices generated possiblities
00 00 00 00 01 01 01 01 10 10 10 10 11 11 11 11
00 01 10 11 00 01 10 11 00 01 11 10 00 01 10 11
"""
blocks = [' ', '▗', '▖', '▄', '▝', '▐', '▞', '▟', '▘', '▚', '▙', '▌', '▀', '▜', '▛', '█']
given = (
(0,1),
(1,0)
)
print(blocks[matrices.index(given)])
output: ▞
These characters, although existing, were not meant to have a direct correlation
of numbers-to-set-1/4 blocks.
So, I have a solution in a published package, and it is not necessarily
more "elegant" than yours, as it is far more verbose.
However, the code around it allows one to "draw" on a text terminal
using these 1/4 blocks as pixels, in a somewhat clean API.
So, this is the class I use to set/reset pixels in a character block. The relevant methods can be used straight from the class, and they take the"pixel coordinates", and the current character block upon which to set or reset the addressed pixel. The code instantiates the class just to be able to use the in operator to check for block-characters.
The project can be installed with "pip install terminedia".
The function and class bellow, extracted from the project, will work in standalone to do the same as you do:
# Snippets from jsbueno/terminedia, v. 0.2.0
def _mirror_dict(dct):
"""Creates a new dictionary exchanging values for keys
Args:
- dct (mapping): Dictionary to be inverted
"""
return {value: key for key, value in dct.items()}
class BlockChars_:
"""Used internaly to emulate pixel setting/resetting/reading inside 1/4 block characters
Contains a listing and other mappings of all block characters used in order, so that
bits in numbers from 0 to 15 will match the "pixels" on the corresponding block character.
Although this class is purposed for internal use in the emulation of
a higher resolution canvas, its functions can be used by any application
that decides to manipulate block chars.
The class itself is stateless, and it is used as a single-instance which
uses the name :any:`BlockChars`. The instance is needed so that one can use the operator
``in`` to check if a character is a block-character.
"""
EMPTY = " "
QUADRANT_UPPER_LEFT = '\u2598'
QUADRANT_UPPER_RIGHT = '\u259D'
UPPER_HALF_BLOCK = '\u2580'
QUADRANT_LOWER_LEFT = '\u2596'
LEFT_HALF_BLOCK = '\u258C'
QUADRANT_UPPER_RIGHT_AND_LOWER_LEFT = '\u259E'
QUADRANT_UPPER_LEFT_AND_UPPER_RIGHT_AND_LOWER_LEFT = '\u259B'
QUADRANT_LOWER_RIGHT = '\u2597'
QUADRANT_UPPER_LEFT_AND_LOWER_RIGHT = '\u259A'
RIGHT_HALF_BLOCK = '\u2590'
QUADRANT_UPPER_LEFT_AND_UPPER_RIGHT_AND_LOWER_RIGHT = '\u259C'
LOWER_HALF_BLOCK = '\u2584'
QUADRANT_UPPER_LEFT_AND_LOWER_LEFT_AND_LOWER_RIGHT = '\u2599'
QUADRANT_UPPER_RIGHT_AND_LOWER_LEFT_AND_LOWER_RIGHT = '\u259F'
FULL_BLOCK = '\u2588'
# This depends on Python 3.6+ ordered behavior for local namespaces and dicts:
block_chars_by_name = {key: value for key, value in locals().items() if key.isupper()}
block_chars_to_name = _mirror_dict(block_chars_by_name)
blocks_in_order = {i: value for i, value in enumerate(block_chars_by_name.values())}
block_to_order = _mirror_dict(blocks_in_order)
def __contains__(self, char):
"""True if a char is a "pixel representing" block char"""
return char in self.block_chars_to_name
#classmethod
def _op(cls, pos, data, operation):
number = cls.block_to_order[data]
index = 2 ** (pos[0] + 2 * pos[1])
return operation(number, index)
#classmethod
def set(cls, pos, data):
""""Sets" a pixel in a block character
Args:
- pos (2-sequence): coordinate of the pixel inside the character
(0,0) is top-left corner, (1,1) bottom-right corner and so on)
- data: initial character to be composed with the bit to be set. Use
space ("\x20") to start with an empty block.
"""
op = lambda n, index: n | index
return cls.blocks_in_order[cls._op(pos, data, op)]
#classmethod
def reset(cls, pos, data):
""""resets" a pixel in a block character
Args:
- pos (2-sequence): coordinate of the pixel inside the character
(0,0) is top-left corner, (1,1) bottom-right corner and so on)
- data: initial character to be composed with the bit to be reset.
"""
op = lambda n, index: n & (0xf - index)
return cls.blocks_in_order[cls._op(pos, data, op)]
#classmethod
def get_at(cls, pos, data):
"""Retrieves whether a pixel in a block character is set
Args:
- pos (2-sequence): The pixel coordinate
- data (character): The character were to look at blocks.
Raises KeyError if an invalid character is passed in "data".
"""
op = lambda n, index: bool(n & index)
return cls._op(pos, data, op)
#: :any:`BlockChars_` single instance: enables ``__contains__``:
BlockChars = BlockChars_()
After pasting only this in the terminal it is possible to do:
In [131]: pixels = BlockChars.set((0,0), " ")
In [132]: print(BlockChars.set((1,1), pixels))
# And this internal "side-product" is closer to what you have posted:
In [133]: BlockChars.blocks_in_order[0b1111]
Out[133]: '█'
In [134]: BlockChars.blocks_in_order[0b1010]
Out[134]: '▐'
The project at https://github.com/jsbueno/terminedia have a complete
drawing API do use these as pixels in an ANSI text terminal -
including bezier curves, filled ellipses, and RGB image display
(check the "examples" folder)
I am currently runnuing training in matlab on a matrix of logspecrum samples I am constantly dealing with underflow problems.I understood that I need to work with log's in order to deal with underflowing.
I am still strugling with uderflow though , when i calculate the mean (mue) bucause it is negetive i cant work with logs so i need the real values that underflow.
These are equasions i am working with:
In MATLAB code i calulate log_tau in oreder avoid underflow but when calulating mue i need exp(log(tau)) which goes to zero.
I am attaching relevent MATLAB code
**in the code i called the variable alpha is tau ...
for i = 1 : 50
log_c = Logsum(log_alpha,1) - log(N);
c = exp(log_c);
mue = DataMat*alpha./(repmat(exp(Logsum(log_alpha,1)),FrameSize,1));
log_abs_mue = log(abs(mue));
log_SigmaSqr = log((DataMat.^2)*alpha) - repmat(Logsum(log_alpha,1),FrameSize,1) - 2*log_abs_mue;
SigmaSqr = exp(log_SigmaSqr);
for j=1:N
rep_DataMat(:,:,j) = repmat(DataMat(:,j),1,M);
log_gamma(j,:) = log_c - 0.5*(FrameSize*log(2*pi)+sum(log_SigmaSqr)) + sum((rep_DataMat(:,:,j) - mue).^2./(2*SigmaSqr));
end
log_alpha = log_gamma - repmat(Logsum(log_gamma,2),1,M);
alpha = exp(log_alpha);
end
c = exp(log_c);
SigmaSqr = exp(log_SigmaSqr);
does any one see how i can avoid this? or what needs to be fixed in code?
What i did was add this line to the MATLAB code:
mue(isnan(mue))=0; %fix 0/0 problem
and this one:
SigmaSqr(SigmaSqr==0)=1;%fix if mue_k = x_k
not sure if this is the best solution but is seems to work...
any have a better idea?
I have the following gabor filter to extract image texture feature..
a=imread('image0001.jpg');
a=double(a);
a=a-mean(a(:));
[r,c,l]=size(a);
K=5; S=6;
Uh=0.4;
Ul=0.05;
alpha=(Uh/Ul)^(1/(S-1));
sigmau=(alpha-1)*Uh/((alpha+1)*sqrt(2*log(2)));
sigmav=tan(pi/(2*K))*(Uh-2*log(2)*((sigmau^2)/Uh))/sqrt((2*log(2))-(((2*log(2))^2)*(sigmau^2)/(Uh^2)));
sigmax=1/(2*pi*sigmau);
sigmay=1/(2*pi*sigmav);
b=fft2(a);
[e d]=size(b);
i=1;
G=zeros(r,c,S*K);
IZ=zeros(r,c,S*K);
for m=0:S-1
for n=0:K-1
fprintf(1,'.');
for x=-r/2+1:r/2;
for y=-c/2+1:c/2;
xdash=(alpha^(-m))*((x)*cos(n*pi/K)+(y)*sin(n*pi/K));
ydash=(alpha^(-m))*((y)*cos(n*pi/K)-(x)*sin(n*pi/K));
g(r/2+x,r/2+y)=(alpha^(-m))*((1/(2*pi*sigmax*sigmay))*exp(-0.5*(((xdash^2)/(sigmax^2))+((ydash^2)/(sigmay^2)))+0.8i*pi*xdash));
end
end
[rr cc]=size(g);
G(:,:,i)=g;
h=fft2(g);
z=b.*h;
iz=ifft2(z);
IZ(:,:,i)=iz;
FeatureVector(i)=mean(abs(iz(:)));
i=i+1;
end
end
fprintf(1,'\n');
%%%%%%%%%
When I run this code I get this Error:
Error using ==> times Matrix
dimensions must agree. Error in ==>
ComputeGaborFeatures4 at 37
z=b.*h;
Please if any one can help me to solve this error or any one can give me another simple gabor filter?
The error is due to the calling of Array Multiplication (.*) with b and h of non equal size, because rr doesn't equal r and cc doesn't c.
Either you wanted to use Matrix Multiplication (*) or you need to make g and a the same size before calling fft2.
the error might change the g(r/2+x,r/2+y) to g(r/2+x,c/2+y), the the