I'm trying to port the code from flipbook component in Flex to Air 1.5.1, it gave this error
1137: Incorrect number of arguments. Expected no more than 0.
delta = new Vector(new Point(x,_pointOfOriginalGrab.y),new Point(x+10,_pointOfOriginalGrab.y+1));
How do I make it work in Air?
From the Flex documentation, it looks like Vector constructor accepts a length for it's first argument and fixed for its second argument. You can rewrite you code as this:
delta = new Vector();
delta.push(new Point(x,_pointOfOriginalGrab.y));
delta.push(new Point(x+10,_pointOfOriginalGrab.y+1));
Here's the documentation page:
Vector Documentation
Related
When I run a Xcos model containing a scifunc_block_m block like shown below
I get an error message relating to data dimensions inconsistency:
"Data dimensions are inconsistent:"
" Variable size=[1,1]"
"Block output size=[100,1]."
But when I double click in the block in order to see what can I change to make the dimensions correct I get a message in the console saying
Undefined variable: scifunc_block_m
What bugs me is that scifunc_block_m is not the name of any variable, but rather the name of the block itself like can be seen in the official docs.
Of course I double checked that nowhere in my function phase_shifter neither anywhere else I have any variable named like that.
I tried with Scilab 6.1.1 and 6.1.0 believing that it might be a bug from apparently not.
In your phase_shifter.sce file generating the input variable,
the signalIn variable does not comply with the From Workspace block requirements, whose documentation says that the input variable
must be a structure with time and values fields
.time must be a column vector, and in your case
.values must also be a column
So,
t = (0:1/fs:Npp/fs - 1/fs); // time vector
signalIn = A*%e^(%i*w*t);
should be replaced with
t = (0:1/fs:Npp/fs - 1/fs)'; // time column vector
signalIn = struct("time",t, "values",A*%e^(%i*w*t));
This fixes the inconsistent dimensions message.
In addition, i am not able to reproduce your issue about Undefined variable: scifunc_block_m. The parameters interface opens as expected.
You may get this kind of messages if you try to run some xcos parts out of xcos, without beforehand loading xcos-related libraries.
Then, we get an unclear "Output should be of complex type." message on the From workspace block.
By the way, you try to plot some complex values. Please have a look to the MATMAGPHI block before entering MUX: https://help.scilab.org/docs/6.1.1/en_US/MATMAGPHI.html
I have been learning Tensorflow and understanding feed_dict has been a challenge. Take for example the following piece of code i am working on
p=0
self.sequence_length=25
with tf.Session() as sess:
init.run()
char_to_ix={ch:ix for ix,ch in enumerate(self.words)}
ix_to_char={ix:ch for ix,ch in enumerate(self.words)}
words_in_input=self.data[p:p+self.sequence_length]
inputs=[char_to_ix[ix] for ix in words_in_input]
words_in_target=self.data[p+1:p+self.sequence_length+1]
targets=[char_to_ix[ix] for ix in words_in_target]
onex=sess.run([selected_next_letter],feed_dict={self.X:inputs,self.y:targets})
p=p+1
This gives the error: Shapes of all inputs must match: values[0].shape = [25] != values[1].shape = []
However, when I edit the code to
with tf.Session() as sess:
init.run()
char_to_ix={ch:ix for ix,ch in enumerate(self.words)}
ix_to_char={ix:ch for ix,ch in enumerate(self.words)}
words_in_input=self.data[p:p+self.sequence_length]
inputs=[char_to_ix[ix] for ix in words_in_input]
words_in_target=self.data[p+1:p+self.sequence_length+1]
targets=[char_to_ix[ix] for ix in words_in_target]
for x,y in zip(inputs,targets):
onex=sess.run([selected_next_letter],feed_dict={self.X:x,self.y:y})
It executes.
My questions is: Is it possible to feed the whole list such as inputs and targets in the feed_dict or must I input it through a loop one by one. I ask this because the tutorials I have been reading, I see a whole list being passed in a feed_dict such as
loss_val = sess.run([train_op, loss_mean], feed_dict={
images_batch:images_batch_val,
labels_batch:labels_batch_val
})
Usually the reason for that error is because your input array(x) isn’t the same size as your labels array(y). As the error states it looks like your labels array is empty. Before doing anything tensorflowy make sure both x and y arrays have values in them and that they are of the same size.
To answer your question, yes you can use lists when training and is the preferred way of using tensorflow.
I am trying to build the inline segment to filter the pages (ex. to separate the pages for blogs and games) using the function BuildClassificationValueSegment() to get the data from Adobe Analytics API,
I have tried some thing like
report.data.visits <- QueueTrended(reportsuite.id,date.from,date.to,metrics,elements,
segment.inline = BuildClassificationValueSegment("evar2","blog","OR")).
Got error like :
Error in ApiRequest(body = report.description, func.name = "Report.Validate") :
ERROR: segment_invalid - Segment "evar2" not valid for this company
In addition: Warning message:
In if (segment.inline != "") { :
the condition has length > 1 and only the first element will be used
Please help on the same.Thanks in advance...
I recommend you to declare the InlineSegment in advance and store it in a variable. Then pass it to the QueueTrended function.
I've been using the following syntax to generate an inline segment:
InlineSegment <- list(container=list(type=unbox("hits"),
rules=data.frame(
name=c("Page Name(eVar48)"),
element=c("evar48"),
operator=c("equals"),
value=c(as.character("value1","value2"))
))
You can change the name and element arguments in order to personalize the query.
The next step is to pass the InlineSegment to the QueueRanked function:
Report <- as.data.frame(QueueRanked("reportsuite",
date.from = dateStart,
date.to = dateEnd,
metrics = c("pageviews"),
elements = c("element"),
segment.inline = InlineSegment,
max.attempts=500))
I borrowed that syntax from this thread some time ago: https://github.com/randyzwitch/RSiteCatalyst/issues/129
Please note that there might be easier ways to obtain this kind of report without using InlineSegmentation. Maybe you can use the selected argument from the QueueRanked function in order to narrow down the scope of the report.
Also, I'm purposefully avoiding the BuildClassificationValueSegment function as I found it a bit difficult to understand.
Hope this workaround helps...
Upon trying to calculate precision#k, I get an exception. To what follows is the a simple code that reproduces the problem.
First the code defines the variable scope:
initializer = tf.random_uniform_initializer(-0.1, 0.1, seed=1234)
with tf.variable_scope("model", reuse=None, initializer=initializer)
Then it calls those lines:
predictions = tf.Variable(tf.ones([2, 10], tf.int64))
labels = tf.Variable(tf.ones([2, 1], tf.int64))
precision = tf.contrib.metrics.streaming_sparse_precision_at_k(predictions, labels, 5)
tf.initialize_all_variables().run()
(I know this code is meaningless, and tries to calculate the precision given 2 fixed matrices...)
Then I get the following exception:
W tensorflow/core/framework/op_kernel.cc:936] Failed precondition:
Attempting to use uninitialized value
model/precision_at_5/false_positive_at_5 [[Node:
model/precision_at_5/false_positive_at_5/read = IdentityT=DT_DOUBLE,
_class=["loc:#model/precision_at_5/false_positive_at_5"], _device="/job:localhost/replica:0/task:0/gpu:0"]]
The same goes when I tried to invoke streaming_sparse_recall_at_k instead of streaming_sparse_precision_at_k.
The installed version is r0.10 on linux with python 2.7.
Please help... Thanks in advance :)
Unfortunately, tf.initialize_all_variables() doesn't initialize "local" variables (which tend to be internal implementation details for ops like tf.contrib.metrics.streaming_sparse_precision_at_k() and tf.train.string_input_producer(), as opposed to variables used as model weights).
You'll need to add a line to your program that runs tf.initialize_local_variables() before running the evaluation op:
sess.run(tf.initialize_local_variables()) # or `tf.initialize_local_variables().run()`
Hi I've tried this a couple of ways and I'm not sure what I'm missing. The documentation here https://developers.google.com/maps/documentation/javascript/reference states that I should be able to set the center of my Map using the coordinates like this:
setCenter(latlng:LatLng)
which I am guessing means I can use the syntax:
myMap.setCenter(-37.8025182,144.9987055);
but I get the error "Uncaught Error: Invalid value for property : -37.8025182 "
Is there something I'm missing - the documentation doesn't really give me any clues.
Regards,
Lea.
The documentation says the single argument for setCenter is a LatLng. setCenter doesn't take two Numbers.
var centerpoint = new google.maps.LatLng(-37.8025182,144.9987055);
myMap.setCenter(centerpoint);
or combine everything together:
myMap.setCenter(new google.maps.LatLng(-37.8025182,144.9987055));