This is my code in theano
max_max=200
beReplaced=T.matrix()
toReplace=T.matrix()
timeArray=T.arange(max_max)
def f(v,k,w):
return T.concatenate([w[:k],v,w[k+1:]],axis=0)
result,_=theano.scan(f,
sequences=[toReplace,timeArray],
outputs_info=beReplaced)
What I am trying to do is replace beReplaced with toReplace line by line. The way I do it is by concatenate the upper part of w, v and lower parter of w.
vis lines of toReplace.
Here is the error report
Traceback (most recent call last):
File "/Users/qiansteven/Desktop/NLP/RNN/my.py", line 20, in <module>
outputs_info=np.zeros((5,5),dtype=np.float64))
File "/usr/local/lib/python2.7/site-packages/theano/scan_module/scan.py", line 745, in scan
condition, outputs, updates = scan_utils.get_updates_and_outputs(fn(*args))
File "/Users/qiansteven/Desktop/NLP/RNN/my.py", line 16, in f
return T.concatenate([a,b,c],axis=0)
File "/usr/local/lib/python2.7/site-packages/theano/tensor/basic.py", line 4225, in concatenate
return join(axis, *tensor_list)
File "/usr/local/lib/python2.7/site-packages/theano/gof/op.py", line 611, in __call__
node = self.make_node(*inputs, **kwargs)
File "/usr/local/lib/python2.7/site-packages/theano/tensor/basic.py", line 3750, in make_node
axis, tensors, as_tensor_variable_args, output_maker)
File "/usr/local/lib/python2.7/site-packages/theano/tensor/basic.py", line 3816, in _make_node_internal
raise TypeError("Join() can only join tensors with the same "
TypeError: Join() can only join tensors with the same number of dimensions.
What's wrong???????????
Put toReplace into non_sequences, otherwise each timestep will only take a slice of it. Theano will report error when it tries to concatenate a vector with matrix.
def f(k,w,v): #NOTE the argument order change
return T.concatenate([w[:k],v,w[k+1:]],axis=0)
result,_=theano.scan(f,
sequences=timeArray,
outputs_info=beReplaced,
non_sequences=toReplace)
The solution is to concatenate v.dimshuffle('x',0) and that solves the dim problem.
Related
I have a project which is OpenCV python lane dedection, i can easily run code with video(importing VideoFileClip in moviepy.editor). Actually when i try to convert video file to real time using VideoCapture. I can get this error:
Traceback (most recent call last):
File "advanced_lane_finding(1).py", line 619, in <module>
a=process(frame)
File "advanced_lane_finding(1).py", line 575, in process
left_poly = fit_poly(binary,llo, lhi, process.old_left_points)
File "advanced_lane_finding(1).py", line 462, in fit_poly
poly = np.poly1d(np.polyfit(pts[0],pts[1],2))
File "<__array_function__ internals>", line 5, in polyfit
File "/usr/lib/python3/dist-packages/numpy/lib/polynomial.py", line 601, in polyfit
raise TypeError("expected non-empty vector for x")
TypeError: expected non-empty vector for x
I want to run with my webcam my code
I copied the code from datacamp to try the Naive Bayes classification on my own on python 3.8 . but when run the code the compiler gives this error
Traceback (most recent call last):
File "c:\Users\USER\Desktop\DATA MINING\NaiveTest.py", line 34, in <module>
model.fit(features,label)
File "C:\Users\USER\AppData\Local\Programs\Python\Python38-32\lib\site-packages\sklearn\naive_bayes.py", line 207, in fit
X, y = self._validate_data(X, y)
File "C:\Users\USER\AppData\Local\Programs\Python\Python38-32\lib\site-packages\sklearn\base.py", line 433, in _validate_data
X, y = check_X_y(X, y, **check_params)
File "C:\Users\USER\AppData\Local\Programs\Python\Python38-32\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "C:\Users\USER\AppData\Local\Programs\Python\Python38-32\lib\site-packages\sklearn\utils\validation.py", line 814, in check_X_y
X = check_array(X, accept_sparse=accept_sparse,
File "C:\Users\USER\AppData\Local\Programs\Python\Python38-32\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f
return f(*args, **kwargs)
File "C:\Users\USER\AppData\Local\Programs\Python\Python38-32\lib\site-packages\sklearn\utils\validation.py", line 630, in check_array
raise ValueError(
ValueError: Expected 2D array, got scalar array instead:
array=<zip object at 0x0F2C4C28>.
Reshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample.
I am posting the whole code cause I'm not sure which part that causes this so I'm requesting help to solve this.
# Assigning features and label variables
weather=['Sunny','Sunny','Overcast','Rainy','Rainy','Rainy','Overcast','Sunny','Sunny','Rainy','Sunny','Overcast','Overcast','Rainy']
temp=['Hot','Hot','Hot','Mild','Cool','Cool','Cool','Mild','Cool','Mild','Mild','Mild','Hot','Mild']
play=['No','No','Yes','Yes','Yes','No','Yes','No','Yes','Yes','Yes','Yes','Yes','No']
# Import LabelEncoder
from sklearn import preprocessing
#creating labelEncoder
le = preprocessing.LabelEncoder()
# Converting string labels into numbers.
weather_encoded=le.fit_transform(weather)
print (weather_encoded)
temp_encoded=le.fit_transform(temp)
label=le.fit_transform(play)
print ("Temp:",temp_encoded)
print ("Play:",label)
#Combinig weather and temp into single listof tuples
features=zip(weather_encoded,temp_encoded)
print(list(zip(weather_encoded,temp_encoded)))
print([i for i in zip(weather_encoded,temp_encoded)])
from sklearn.naive_bayes import GaussianNB
#Create a Gaussian Classifier
model = GaussianNB()
# Train the model using the training sets
model.fit(features,label)
#Predict Output
predicted= model.predict([[0,2]]) # 0:Overcast, 2:Mild
print ("Predicted Value:", predicted)
supposedly the result something like this Predicted Value: [1]
but it gave this error instead
What happens is that features should be a list to be passed to model.fit, currently they are type zip
#Combinig weather and temp into single listof tuples
features=zip(weather_encoded,temp_encoded)
you may need to convert features to list, e.g.
#Combinig weather and temp into single listof tuples
features=list(zip(weather_encoded,temp_encoded))
Traceback (most recent call last):
File "C:/Users/Lenovo/PycharmProjects/ProjetFinal/venv/turkish.py", line 45, in <module>
sequences_matrix = sequence.pad_sequences(sequences,maxlen=max_len)
File "C:\Users\Lenovo\PycharmProjects\ProjetFinal\venv\lib\site-packages\keras_preprocessing\sequence.py", line 96, in pad_sequences
trunc = np.asarray(trunc, dtype=dtype)
File "C:\Users\Lenovo\PycharmProjects\ProjetFinal\venv\lib\site-packages\numpy\core\_asarray.py", line 85, in asarray
return array(a, dtype, copy=False, order=order)
ValueError: invalid literal for int() with base 10:
windows loading yapıo sonra mavi ekran iste. simdi repair yapıyo bkalım olmazsa
gelirim size.
X = dft.text
your straight using text with pad_sequence. this is how pad_sequence doesn't work
Generally, every text is converted into numbers within a given vocab which can be of characters or words depend upon task.
then it can be padded.
please refer some tutorial through you can understand how to process text first.
Tensorflow tutorials - text part are good to start.
https://www.tensorflow.org/guide/keras/masking_and_padding
I am experiencing difficulty in this area. I experienced ValueError in the following: (I have tried solutions online but to no avail)
Here's my original code, which returns Convert String to Float error
ValueError: could not convert string to float: '3,1,0,0,0,1,0,1,89874,49.99'):
from sklearn.preprocessing import MinMaxScaler
import pandas as pd
training_data_df = pd.read_csv('./data/sales_data_training.csv')
scaler = MinMaxScaler(feature_range=(0,1))
scaled_training= scaler.fit_transform(training_data_df)
scaled_training_df = pd.DataFrame(scaled_training,columns= training_data_df.columns.values)
My CSV Data:
"critic_rating,is_action,is_exclusive_to_us,is_portable,is_role_playing,is_sequel,is_sports,suitable_for_kids,total_earnings,unit_price"
"3.5,1,0,1,0,1,0,0,132717,59.99"
"4.5,0,0,0,0,1,1,0,83407,49.99"...
'3,1,0,0,0,1,0,1,89874,49.99'
I have 9 columns of data across 1000 rows (~9999 data, with first row being the header).
Regards,
Yuki
The full error is as follows:
Traceback (most recent call last):
File "C:/Users/YukiKawaii/PycharmProjects/PandasTest/module2_NN/test.py", line 6, in <module>
scaled_training= scaler.fit_transform(training_data_df)
File "C:\Users\YukiKawaii\Python\Python35\lib\site-packages\sklearn\base.py", line 517, in fit_transform
return self.fit(X, **fit_params).transform(X)
File "C:\Users\YukiKawaii\Python\Python35\lib\site-packages\sklearn\preprocessing\data.py", line 308, in fit
return self.partial_fit(X, y)
File "C:\Users\YukiKawaii\Python\Python35\lib\site-packages\sklearn\preprocessing\data.py", line 334, in partial_fit
estimator=self, dtype=FLOAT_DTYPES)
File "C:\Users\YukiKawaii\Python\Python35\lib\site-packages\sklearn\utils\validation.py", line 433, in check_array
array = np.array(array, dtype=dtype, order=order, copy=copy)
ValueError: could not convert string to float: '3,1,0,0,0,1,0,1,89874,49.99'
You should remove the "" and '' wrapped around each line in the csv file.
By default pd.read_csv() splits each line by , and thus it cannot convert strings to floats if the "" and '' were there.
So the csv file should look as follows.
critic_rating,is_action,is_exclusive_to_us,is_portable,is_role_playing,is_sequel,is_sports,suitable_for_kids,total_earnings,unit_price
3.5,1,0,1,0,1,0,0,132717,59.99
4.5,0,0,0,0,1,1,0,83407,49.99
3,1,0,0,0,1,0,1,89874,49.99
I just verified by running your code after making the above change.
I have two Series objects, which from my perspective look exactly the same, except they contain different data. I have attempted to convert them to DataFrames and to put them both in the same DataFrame as separate columns. For some reason I cannot fathom, one of the Series will be converted happily to a DataFrame and the other one refuses to be converted when placed in a container (list or dict). I get a reindexing error, but there are no duplicates in the index of either Series.
import pickle
import pandas as pd
s1 = pickle.load(open('s1.p', 'rb'))
s2 = pickle.load(open('s2.p', 'rb'))
print(s1.head(10))
print(s2.head(10))
pd.DataFrame(s1) # <--- works fine
pd.DataFrame(s2) # <--- works fine
pd.DataFrame([s1]) # <--- works fine
# pd.DataFrame([s2]) # <--- doesn't work
# pd.DataFrame([s1, s2]) # <--- doesn't work
pd.DataFrame({s1.name: s1}) # <--- works fine
pd.DataFrame({s2.name: s2}) # <--- works fine
pd.DataFrame({s1.name: s1, s2.name: s1}) # <--- works fine
# pd.DataFrame({s1.name: s1, s2.name: s2}) # <--- doesn't work
Here is the output, although you can't see it here, there is overlap between the index values; they are just in a different order. I want the indexes to be matched when I combine them into the same DataFrame.
id
801120 42.01
801138 50.18
801139 50.01
802101 53.77
802110 56.52
802112 47.37
802113 46.52
802114 46.58
802115 42.59
802117 40.85
Name: age, dtype: float64
id
A32067 0.39083
A32195 0.28506
A01685 0.36432
A11124 0.55649
A32020 0.41524
A32021 0.43788
A32098 0.49206
A00699 0.37515
A32158 0.58793
A14139 0.47413
Name: lh_vtx_000001, dtype: float64
Traceback when the final line is uncommented:
Traceback (most recent call last):
File "/Users/sm2286/Documents/Vertex/test.py", line 18, in <module>
pd.DataFrame({s1.name: s1, s2.name: s2}) # <--- doesn't work
File "/Users/sm2286/anaconda3/lib/python3.5/site-packages/pandas/core/frame.py", line 224, in __init__
mgr = self._init_dict(data, index, columns, dtype=dtype)
File "/Users/sm2286/anaconda3/lib/python3.5/site-packages/pandas/core/frame.py", line 360, in _init_dict
return _arrays_to_mgr(arrays, data_names, index, columns, dtype=dtype)
File "/Users/sm2286/anaconda3/lib/python3.5/site-packages/pandas/core/frame.py", line 5236, in _arrays_to_mgr
arrays = _homogenize(arrays, index, dtype)
File "/Users/sm2286/anaconda3/lib/python3.5/site-packages/pandas/core/frame.py", line 5534, in _homogenize
v = v.reindex(index, copy=False)
File "/Users/sm2286/anaconda3/lib/python3.5/site-packages/pandas/core/series.py", line 2287, in reindex
return super(Series, self).reindex(index=index, **kwargs)
File "/Users/sm2286/anaconda3/lib/python3.5/site-packages/pandas/core/generic.py", line 2229, in reindex
fill_value, copy).__finalize__(self)
File "/Users/sm2286/anaconda3/lib/python3.5/site-packages/pandas/core/generic.py", line 2247, in _reindex_axes
copy=copy, allow_dups=False)
File "/Users/sm2286/anaconda3/lib/python3.5/site-packages/pandas/core/generic.py", line 2341, in _reindex_with_indexers
copy=copy)
File "/Users/sm2286/anaconda3/lib/python3.5/site-packages/pandas/core/internals.py", line 3586, in reindex_indexer
self.axes[axis]._can_reindex(indexer)
File "/Users/sm2286/anaconda3/lib/python3.5/site-packages/pandas/indexes/base.py", line 2293, in _can_reindex
raise ValueError("cannot reindex from a duplicate axis")
ValueError: cannot reindex from a duplicate axis
Traceback when line 13 is uncommented:
Traceback (most recent call last):
File "/Users/sm2286/Documents/Vertex/test.py", line 13, in <module>
pd.DataFrame([s2]) # <--- doesn't work
File "/Users/sm2286/anaconda3/lib/python3.5/site-packages/pandas/core/frame.py", line 263, in __init__
arrays, columns = _to_arrays(data, columns, dtype=dtype)
File "/Users/sm2286/anaconda3/lib/python3.5/site-packages/pandas/core/frame.py", line 5359, in _to_arrays
dtype=dtype)
File "/Users/sm2286/anaconda3/lib/python3.5/site-packages/pandas/core/frame.py", line 5453, in _list_of_series_to_arrays
indexer = indexer_cache[id(index)] = index.get_indexer(columns)
File "/Users/sm2286/anaconda3/lib/python3.5/site-packages/pandas/indexes/base.py", line 2082, in get_indexer
raise InvalidIndexError('Reindexing only valid with uniquely'
pandas.indexes.base.InvalidIndexError: Reindexing only valid with uniquely valued Index objects
After more investigation the difference between the Series was that the latter contained missing values. Removing them fixed the issue.