I'm trying to implement a simple model in OpenModelica, but I got an overnumber of equations caused by the connector.
The tools is composed by three items:
The first is the connector, in which only a flow is defined:
```
connector ConnectorMassFlow
flow Real G "Rate [kg/s]";
end ConnectorMassFlow;
```
The second is a compressor model, in which 1 connector is defined:
```
model ModelCompressor
ConnectorMassFlow MassFlowIn;
equation
end ModelCompressor;
```
And then the circuit model, in which the compressor is defined, and another connector as well:
```
model ModelConditioner
ConnectorMassFlow MassFlowIn;
ModelCompressor Compressor;
equation
MassFlowIn.G = 1.0;
connect(Compressor.MassFlowIn,MassFlowIn);
end ModelConditioner;
```
The problem is that, even if there should be only 2 equations, i.e.:
Compressor.MassFlowIn.G - MassFlowIn.G = 0.0;
MassFlowIn.G = 1.0;
OpenModelica is adding another equation, that is:
MassFlowIn.G = 0.0;
In addition, in the first equation there should be a + between the two terms, and not a minus...
Can someone help me please?
Related
I am trying to learn what the various outputs of predict.coxph() mean. I am currently attempting to fit a cox model on a training set then use the resulting coefficients from the training set to make predictions in a test set (new set of data).
I see from the predict.coxph() help page that I could use type = "survival" to extract and individual's survival probability-- which is equal to exp(-expected).
Here is a code block of what I have attempted so far, using the ISLR2 BrainCancer data.
set.seed(123)
n.training = round(nrow(BrainCancer) * 0.70) # 70:30 split
idx = sample(1:nrow(BrainCancer), size = n.training)
d.training = BrainCancer[idx, ]
d.test = BrainCancer[-idx, ]
# fit a model using the training set
fit = coxph(Surv(time, status) ~ sex + diagnosis + loc + ki + gtv + stereo, data = d.training)
# get predicted survival probabilities for the test set
pred = predict(fit, type = "survival", newdata = d.test)
The predictions generated:
predict(fit, type = "survival", newdata = d.test)
[1] 0.9828659 0.8381164 0.9564982 0.2271862 0.2883800 0.9883625 0.9480138 0.9917512 1.0000000 0.9974775 0.7703657 0.9252100 0.9975044 0.9326234 0.8718161 0.9850815 0.9545622 0.4381646 0.8236644
[20] 0.2455676 0.7289031 0.9063336 0.9126897 0.9988625 0.4399697 0.9360874
Are these survival probabilities associated with a specific time point? From the help page, it sounds like these are survival probabilities at the follow-up times in the newdata argument. Is this correct?
Additional questions:
How is the baseline hazard estimated in predict.coxph? Is it using the Breslow estimator?
If type = "expected" is used, are these values the cumulative hazard? If yes, what are the relevant time points for these?
Thank you!
I have recently started exploring the QuantLib option pricing libraries for python and have come across an error that I don't seem to understand. Basically, I am trying to price an Up&Out Barrier option using the Heston model. The code that I have written has been taken from examples found online and adapted to my specific case. Essentially, the problem is that when I run the code below I get an error that I believe is triggered at the last line of the code, i.e. the european_option.NPV() function
*** RuntimeError: wrong argument type
Can someone please explain me what I am doing wrong?
# option inputs
maturity_date = ql.Date(30, 6, 2020)
spot_price = 969.74
strike_price = 1000
volatility = 0.20
dividend_rate = 0.0
option_type = ql.Option.Call
risk_free_rate = 0.0016
day_count = ql.Actual365Fixed()
calculation_date = ql.Date(26, 6, 2020)
ql.Settings.instance().evaluationDate = calculation_date
# construct the option payoff
european_option = ql.BarrierOption(ql.Barrier.UpOut, Barrier, Rebate,
ql.PlainVanillaPayoff(option_type, strike_price),
ql.EuropeanExercise(maturity_date))
# set the Heston parameters
v0 = volatility*volatility # spot variance
kappa = 0.1
theta = v0
hsigma = 0.1
rho = -0.75
spot_handle = ql.QuoteHandle(ql.SimpleQuote(spot_price))
# construct the Heston process
flat_ts = ql.YieldTermStructureHandle(ql.FlatForward(calculation_date,
risk_free_rate, day_count))
dividend_yield = ql.YieldTermStructureHandle(ql.FlatForward(calculation_date,
dividend_rate, day_count))
heston_process = ql.HestonProcess(flat_ts, dividend_yield,
spot_handle, v0, kappa,
theta, hsigma, rho)
# run the pricing engine
engine = ql.AnalyticHestonEngine(ql.HestonModel(heston_process),0.01, 1000)
european_option.setPricingEngine(engine)
h_price = european_option.NPV()
The problem is that the AnalyticHestonEngine is not able to price Barrier options.
Check here https://www.quantlib.org/reference/group__barrierengines.html for a list of Barrier Option pricing engines.
can anyone enlighten me why the following modelica code generates an error on OpenModelica 1.12.0? If I remove the last two connect equations, it works fine.
class A
Conn cc[3];
Real a(start=0,fixed=true);
Real b(start=0,fixed=true);
Real c(start=0,fixed=true);
equation
der(a) = 1;
der(b) = 2;
der(c) = 3;
connect(a,cc[1].v);
connect(b,cc[2].v); // Remove this to make it work
connect(c,cc[3].v); // Remove this to make it work
end A;
The expandable connector cc is empty:
expandable connector Conn
end Conn;
The code above generates error on OpenModelica 1.12.0:
[1] 15:07:44 Symbolic Error
Too many equations, over-determined system. The model has 6 equation(s) and 4 variable(s).
[2] 15:07:44 Symbolic Warning
[A: 11:3-11:21]: Equation 5 (size: 1) b = cc[2].v is not big enough to solve for enough variables.
Remaining unsolved variables are:
Already solved: b
Equations used to solve those variables:
Equation 2 (size: 1): der(b) = 2.0
[3] 15:07:44 Symbolic Warning
[A: 12:3-12:21]: Equation 6 (size: 1) c = cc[3].v is not big enough to solve for enough variables.
Remaining unsolved variables are:
Already solved: c
Equations used to solve those variables:
Equation 3 (size: 1): der(c) = 3.0
Basically, I want to have an array of expandable connectors which I can add different type of variables as needed.
Edit 18/08/2018
Regarding I can only connect the "connectors" to an expandable connector, actually I see the modelica spec 3.4 doc says:
All components in an expandable connector are seen as connector instances even if they are not declared as
such [i.e. it is possible to connect to e.g. a Real variable].
So it seems I can connect Real variables to an expandable connector in OpenModelica however, I get an error in JModelica:
Error at line 13, column 11, in file 'A.mo':
Connecting to an instance of a non-connector type is not allowed
Also I can connect Real variables to normal (non-expandable) connectors as well in OpenModeica, but again this is not allowed in JModelica. So tools interpret the language specs differently!
You cannot connect Real variables to the expandable connector, it needs to be connectors. But somehow that doesn't work either, seems to be a bug. What works (tested in OM and Dymola) is this below:
class Expandable
expandable connector Conn
Real v[3];
end Conn;
connector RealOutput = output Real "'output Real' as connector";
Conn cc;
RealOutput a(start=0,fixed=true);
RealOutput b(start=0,fixed=true);
RealOutput c(start=0,fixed=true);
equation
der(a) = 1;
der(b) = 2;
der(c) = 3;
connect(a,cc.v[1]);
connect(b,cc.v[2]);
connect(c,cc.v[3]);
end Expandable;
I am studying 'An Introduction to Statistical Learning' from James et al (2015). In the experiment section, a script to calculate the goodness-of-fit of different subsets using the k-fold cross validation method.
When I try to plot the error coefficients, I get the error:
Error in UseMethod("predict") : no applicable method for 'predict' applied to an object of class "regsubsets"
The script makes too little sense for me to know what I'm doing wrong. Can anyone help me interpret?
library(leaps)
library(ISLR)
k=10
set.seed(1)
folds=sample(1:k,nrow(Hitters),replace=TRUE)
cv.errors=matrix(NA,k,19, dimnames=list(NULL, paste(1:19)))
for(j in 1:k){
best.fit=regsubsets(Salary~.,data=Hitters[folds!=j,],nvmax=19)
for(i in 1:19){
pred=predict(best.fit,Hitters[folds==j,],id=i)
cv.errors[j,i]=mean( (Hitters$Salary[folds==j]-pred)^2)
}
}
mean.cv.errors=apply(cv.errors,2,mean)
mean.cv.errors
par(mfrow=c(1,1))
plot(mean.cv.errors,type='b')
reg.best=regsubsets(Salary~.,data=Hitters, nvmax=19)
coef(reg.best,11)
I ran into the problem too. Hope you found the answer. If not, here is the answer.
I am sure that you already created the function below.
predict.regsubsets <- function(object, newdata, id,...) {
form <- as.formula(object$call[[2]])
mat <- model.matrix(form, newdata)
coefi <- coef(object, id = id)
xvars <- names(coefi)
mat[,xvars]%*%coefi
}
Now you have to change pred=predict(best.fit,Hitters[folds==j,],id=i) to pred <- predict.regsubsets(best.fit, hitters[folds == j, ], id = i)
Hope it helped.
I'm trying to make tensorflow mfcc give me the same results as python lybrosa mfcc
i have tried to match all the default parameters that are used by librosa
in my tensorflow code and got a different result
this is the tensorflow code that i have used :
waveform = contrib_audio.decode_wav(
audio_binary,
desired_channels=1,
desired_samples=sample_rate,
name='decoded_sample_data')
sample_rate = 16000
transwav = tf.transpose(waveform[0])
stfts = tf.contrib.signal.stft(transwav,
frame_length=2048,
frame_step=512,
fft_length=2048,
window_fn=functools.partial(tf.contrib.signal.hann_window,
periodic=False),
pad_end=True)
spectrograms = tf.abs(stfts)
num_spectrogram_bins = stfts.shape[-1].value
lower_edge_hertz, upper_edge_hertz, num_mel_bins = 0.0,8000.0, 128
linear_to_mel_weight_matrix =
tf.contrib.signal.linear_to_mel_weight_matrix(
num_mel_bins, num_spectrogram_bins, sample_rate, lower_edge_hertz,
upper_edge_hertz)
mel_spectrograms = tf.tensordot(
spectrograms,
linear_to_mel_weight_matrix, 1)
mel_spectrograms.set_shape(spectrograms.shape[:-1].concatenate(
linear_to_mel_weight_matrix.shape[-1:]))
log_mel_spectrograms = tf.log(mel_spectrograms + 1e-6)
mfccs = tf.contrib.signal.mfccs_from_log_mel_spectrograms(
log_mel_spectrograms)[..., :20]
the equivalent in librosa:
libr_mfcc = librosa.feature.mfcc(wav, 16000)
the following are the graphs of the results:
I'm the author of tf.signal. Sorry for not seeing this post sooner, but you can get librosa and tf.signal.stft to match if you center-pad the signal before passing it to tf.signal.stft. See this GitHub issue for more details.
I spent a whole 1 day trying to make them match. Even the rryan's solution didn't work for me (center=False in librosa), but I finally found out, that TF and librosa STFT's match only for the case win_length==n_fft in librosa and frame_length==fft_length in TF. That's why rryan's colab example is working, but you can try that if you set frame_length!=fft_length, the amplitudes are very different (although visually, after plotting, the patterns look similar). Typical example - if you choose some win_length/frame_length and then you want to set n_fft/fft_length to the smallest power of 2 greater than win_length/frame_length, then the results will be different. So you need to stick with the inefficient FFT given by your window size... I don't know why it is so, but that's how it is, hopefully it will be helpful for someone.
The output of contrib_audio.decode_wav should be DecodeWav with { audio, sample_rate } and audio shape is (sample_rate, 1), so what is the purpose for getting first item of waveform and do transpose?
transwav = tf.transpose(waveform[0])
No straight forward way, since librosa stft uses center=True which does not comply with tf stft.
Had it been center=False, stft tf/librosa would give near enough results. see colab sniff
But even though, trying to import the librosa code into tf is a big headache. Here is what I started and gave up. Near but not near enough.
def pow2db_tf(X):
amin=1e-10
top_db=80.0
ref_value = 1.0
log10 = 2.302585092994046
log_spec = (10.0/log10) * tf.log(tf.maximum(amin, X))
log_spec -= (10.0/log10) * tf.log(tf.maximum(amin, ref_value))
pow2db = tf.maximum(log_spec, tf.reduce_max(log_spec) - top_db)
return pow2db
def librosa_feature_like_tf(x, sr=16000, n_fft=2048, n_mfcc=20):
mel_basis = librosa.filters.mel(sr, n_fft).astype(np.float32)
mel_basis = mel_basis.reshape(1, int(n_fft/2+1), -1)
tf_stft = tf.contrib.signal.stft(x, frame_length=n_fft, frame_step=hop_length, fft_length=n_fft)
print ("tf_stft", tf_stft.shape)
tf_S = tf.matmul(tf.abs(tf_stft), mel_basis);
print ("tf_S", tf_S.shape)
tfdct = tf.spectral.dct(pow2db_tf(tf_S), norm='ortho'); print ("tfdct", tfdct.shape)
print ("tfdct before cut", tfdct.shape)
tfdct = tfdct[:,:,:n_mfcc];
print ("tfdct afer cut", tfdct.shape)
#tfdct = tf.transpose(tfdct,[0,2,1]);print ("tfdct afer traspose", tfdct.shape)
return tfdct
x = tf.placeholder(tf.float32, shape=[None, 16000], name ='x')
tf_feature = librosa_feature_like_tf(x)
print("tf_feature", tf_feature.shape)
mfcc_rosa = librosa.feature.mfcc(wav, sr).T
print("mfcc_rosa", mfcc_rosa.shape)
For anyone still looking for this: I had a similar problem some time ago: Matching librosa's mel filterbanks/mel spectrogram to a tensorflow implementation. The solution was to use a different windowing approach for the spectrogram and librosa's mel matrix as constant tensor. See here and here.