RunTimeWarning: line 129

[petitvic]

Hi,

I just started using empca and it works great. However in a few instances, I have the following Warning which seems to make the PCA stop:
empca.py:129: RuntimeWarning: invalid value encountered in double_scalars
self.eigvec[k, j] = x.dot(cw) / c.dot(cw)

Any advice on how to get rid of this message?

Thanks in advance.

[sbailey]

Hi.

At first I thought this would be due to NaN or Inf values in one of the vectors (x, cw, or c), but I haven't been able to reproduce that in separate tests. Can you check if the vectors have any funny values? e.g.

if N.any(x != x):
print "x has NaN values"

Alternately, is it possible that some of your variables have weight=0 for every observation?

Stephen

On Feb 19, 2014, at 5:43 AM, petitvic notifications@github.com wrote:

Hi,

I just started using empca and it works great. However in a few instances, I have the following Warning which seems to make the PCA stop:
empca.py:129: RuntimeWarning: invalid value encountered in double_scalars
self.eigvec[k, j] = x.dot(cw) / c.dot(cw)

Any advice on how to get rid of this message?

Thanks in advance.

—
Reply to this email directly or view it on GitHub.

[petitvic]

Hi Stephen,

thanks for your quick answer and the pb was indeed that some of my
observations had all weights set to 0. It works like a charm now!

Victor

On Thu, Feb 20, 2014 at 6:06 AM, sbailey notifications@github.com wrote:

Hi.

At first I thought this would be due to NaN or Inf values in one of the
vectors (x, cw, or c), but I haven't been able to reproduce that in
separate tests. Can you check if the vectors have any funny values? e.g.

if N.any(x != x):
print "x has NaN values"

Alternately, is it possible that some of your variables have weight=0 for
every observation?

Stephen

On Feb 19, 2014, at 5:43 AM, petitvic notifications@github.com wrote:

Hi,

I just started using empca and it works great. However in a few
instances, I have the following Warning which seems to make the PCA stop:
empca.py:129: RuntimeWarning: invalid value encountered in
double_scalars
self.eigvec[k, j] = x.dot(cw) / c.dot(cw)

Any advice on how to get rid of this message?

Thanks in advance.

Reply to this email directly or view it on GitHub.

Reply to this email directly or view it on GitHubhttps://github.com//issues/1#issuecomment-35589696
.

[sbailey]

Glad it worked out. You are the second user who has been confused by the cryptic effect of inputs with all weights=0. I should probably add a check at the beginning for that case and refuse to start the fitting until the input is cleaned up.

Curiosity: what are you using empca for?

Stephen

On Feb 20, 2014, at 2:16, petitvic notifications@github.com wrote:

Hi Stephen,

thanks for your quick answer and the pb was indeed that some of my
observations had all weights set to 0. It works like a charm now!

Victor

On Thu, Feb 20, 2014 at 6:06 AM, sbailey notifications@github.com wrote:

Hi.

At first I thought this would be due to NaN or Inf values in one of the
vectors (x, cw, or c), but I haven't been able to reproduce that in
separate tests. Can you check if the vectors have any funny values? e.g.

if N.any(x != x):
print "x has NaN values"

Alternately, is it possible that some of your variables have weight=0 for
every observation?

Stephen

On Feb 19, 2014, at 5:43 AM, petitvic notifications@github.com wrote:

Hi,

I just started using empca and it works great. However in a few
instances, I have the following Warning which seems to make the PCA stop:
empca.py:129: RuntimeWarning: invalid value encountered in
double_scalars
self.eigvec[k, j] = x.dot(cw) / c.dot(cw)

Any advice on how to get rid of this message?

Thanks in advance.

Reply to this email directly or view it on GitHub.

Reply to this email directly or view it on GitHubhttps://github.com//issues/1#issuecomment-35589696
.

—
Reply to this email directly or view it on GitHub.

[petitvic]

The check would be good for new users I guess.
Regarding my use of empca, we have some NGS Cancer data with several normal
and tumoral samples for each patient. I am visualizing these data per
patient to see whether there is any mix-ups between the normal and tumoral
samples.

Victor

On Thu, Feb 20, 2014 at 4:07 PM, sbailey notifications@github.com wrote:

Glad it worked out. You are the second user who has been confused by the
cryptic effect of inputs with all weights=0. I should probably add a check
at the beginning for that case and refuse to start the fitting until the
input is cleaned up.

Curiosity: what are you using empca for?

Stephen

On Feb 20, 2014, at 2:16, petitvic notifications@github.com wrote:

Hi Stephen,

thanks for your quick answer and the pb was indeed that some of my
observations had all weights set to 0. It works like a charm now!

Victor

On Thu, Feb 20, 2014 at 6:06 AM, sbailey notifications@github.com
wrote:

Hi.

At first I thought this would be due to NaN or Inf values in one of
the
vectors (x, cw, or c), but I haven't been able to reproduce that in
separate tests. Can you check if the vectors have any funny values?
e.g.

if N.any(x != x):
print "x has NaN values"

Alternately, is it possible that some of your variables have weight=0
for
every observation?

Stephen

On Feb 19, 2014, at 5:43 AM, petitvic notifications@github.com
wrote:

Hi,

I just started using empca and it works great. However in a few
instances, I have the following Warning which seems to make the PCA
stop:
empca.py:129: RuntimeWarning: invalid value encountered in
double_scalars
self.eigvec[k, j] = x.dot(cw) / c.dot(cw)

Any advice on how to get rid of this message?

Thanks in advance.

Reply to this email directly or view it on GitHub.

Reply to this email directly or view it on GitHub<
https://github.com/sbailey/empca/issues/1#issuecomment-35589696>
.

Reply to this email directly or view it on GitHub.

Reply to this email directly or view it on GitHubhttps://github.com//issues/1#issuecomment-35630247
.

[sbailey]

Great. Glad to hear that it is getting use beyond astrophysics.

Stephen

On Feb 20, 2014, at 7:17, petitvic notifications@github.com wrote:

The check would be good for new users I guess.
Regarding my use of empca, we have some NGS Cancer data with several normal
and tumoral samples for each patient. I am visualizing these data per
patient to see whether there is any mix-ups between the normal and tumoral
samples.

Victor

On Thu, Feb 20, 2014 at 4:07 PM, sbailey notifications@github.com wrote:

Glad it worked out. You are the second user who has been confused by the
cryptic effect of inputs with all weights=0. I should probably add a check
at the beginning for that case and refuse to start the fitting until the
input is cleaned up.

Curiosity: what are you using empca for?

Stephen

On Feb 20, 2014, at 2:16, petitvic notifications@github.com wrote:

Hi Stephen,

thanks for your quick answer and the pb was indeed that some of my
observations had all weights set to 0. It works like a charm now!

Victor

On Thu, Feb 20, 2014 at 6:06 AM, sbailey notifications@github.com
wrote:

Hi.

At first I thought this would be due to NaN or Inf values in one of
the
vectors (x, cw, or c), but I haven't been able to reproduce that in
separate tests. Can you check if the vectors have any funny values?
e.g.

if N.any(x != x):
print "x has NaN values"

Alternately, is it possible that some of your variables have weight=0
for
every observation?

Stephen

On Feb 19, 2014, at 5:43 AM, petitvic notifications@github.com
wrote:

Hi,

I just started using empca and it works great. However in a few
instances, I have the following Warning which seems to make the PCA
stop:
empca.py:129: RuntimeWarning: invalid value encountered in
double_scalars
self.eigvec[k, j] = x.dot(cw) / c.dot(cw)

Any advice on how to get rid of this message?

Thanks in advance.

Reply to this email directly or view it on GitHub.

Reply to this email directly or view it on GitHub<
https://github.com/sbailey/empca/issues/1#issuecomment-35589696>
.

Reply to this email directly or view it on GitHub.

Reply to this email directly or view it on GitHubhttps://github.com//issues/1#issuecomment-35630247
.

—
Reply to this email directly or view it on GitHub.

[londumas]

The solution is something like this:

mask_empty = sp.sum(ivar,axis=0)>0.
init_size = mask_empty.size
flux = flux[:,mask_empty]
ivar = ivar[:,mask_empty]

run empca, then return:

new_eighenvalue = sp.zeros((nbCoeff,init_size) )
for i in range(nbCoeff):
    new_eighenvalue[i,sp.logical_not(mask_empty)] = eighenvalue[i]