There is a way to generate vector images with matplotlob, outputting with the library io to a vector image (SVG) with this approach. I personally tried to run the code of the approach (generate a vectorial histogram) in that webpage as a python file, and it worked. The code: import numpy as np import matplotlib.pyplot … Read more
As long as you have a path that contains hdfs:/ you will not succeed as that is never going to be valid. In the comments you mentioned that you tried to add hdfs:// manually, which may be a nice hack, but in your code I don’t see you ‘clean up’ the wrong hdfs:/. So even … Read more
Chunk random_days_panel instead of historical_data and use da.map_blocks: def dask_way(sim_count, sim_days, hist_days): # shared historical data # on a cluster you’d load this on each worker, e.g. from a NPZ file historical_data = np.random.normal(111.51, 10, size=hist_days) random_days_panel = da.random.randint( 1, hist_days, size=(1, 1, sim_count, sim_days) ) future_panel = da.map_blocks( lambda chunk: historical_data[chunk], random_days_panel, dtype=float ) … Read more
You should never need to manually set the quantization stats. Have you tried the post-training-quantization tutorials? https://www.tensorflow.org/lite/performance/post_training_integer_quant Basically they set the quantization options: converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] converter.inference_input_type = tf.uint8 converter.inference_output_type = tf.uint8 Then they pass a “representative dataset” to the converter, so that the converter can run the model a few batches to gather the … Read more
Try to approach the problem from the Python side: Python is a great glue language, I would suggest you to have Python run your Matlab and C programs. Python has: Numpy PyLab Matplotlib IPython Thus, the combination is a good alternative for almost any existing Matlab module.
I’m searching for the same answers (lack of documentation makes it difficult). In answer, to question #1, what is the utility of “extra_generated_classes”: tl;dr extra_generated_classes defines a modulename, classname, and parentname used in template generating js/html code, and extends the parent class passed into the app class (usually HBox or VBox in the examples). Longer … Read more
I am by no ways an expert in Tensorflow, but if I had to take a guess as to why this is happening, I would say that: tf.train.Saver(), saves a complete meta-graph. Therefore, all the information needed to perform any operations contained in your graph is already there. All tensorflow needs to do to load … Read more
The solution I propose to solve this challenge is based on repeating the algorithm several times while recordig valid solutions. As solution is not unique, I expect the algorithm to throw more than 1 solution. Each of them will have a score based on neighbours affinity. I’ll call an ‘attempt‘ to a complete run trying … Read more
Apparently it is a bug, if you try this import numpy as np from mpl_toolkits.mplot3d import axes3d import matplotlib.pyplot as plt fig = plt.figure() ax = fig.add_subplot(111, projection=”3d”) X, Y = np.mgrid[-1:1:30j, -1:1:30j] Z = np.sin(np.pi*X)*np.sin(np.pi*Y) ax.plot_surface(X, Y, Z, cmap=”autumn_r”, lw=0, rstride=1, cstride=1) ax.contour(X, Y, Z+1, 10, lw=3, colors=”k”, linestyles=”solid”) plt.show() And rotate around, you … Read more
You could use a function like this: def nan_ints(df, convert_strings=False, subset=None): types = [“int64”, “float64”] if subset is None: subset = list(df) if convert_strings: types.append(“object”) for col in subset: if df[col].dtype in types: df[col] = ( df[col].astype(float, errors=”ignore”).astype(“Int64″, errors=”ignore”) ) return df It iterates through each column and coverts it to an Int64 if it … Read more