I used np.genfromtxt to load a txt file.
Below is the code
datasets = np.genfromtxt('X&YTrainingsets1.txt',delimiter="")
It happens that I got an error
ValueError: Some errors were detected !
Line #1162 (got 5 columns instead of 7)
Line #1163 (got 2 columns instead of 7)
So I had to look through the txt file and I found this. So how do I solve this problem?. Thanks
I used
import pandas as pd
datasets = pd.read_csv('X&YTrainingsets1.txt',delimiter='\s+',index_col=False,header=None)
for i in range(0,len(datasets)):
if datasets.iloc[i].isnull().sum() == 2:
datasets.loc[i][5],datasets.loc[i][6] = datasets.loc[i+1][0],datasets.loc[i+1][1]
elif datasets.iloc[i].isnull().sum() == 1:
datasets.loc[i][6] = datasets.loc[i+1][0]
Related
I want to convert a variable to a string and then to an array that I can use to compare, but i dont know how to do that.
my code:
import face_recognition
import numpy as np
a = face_recognition.load_image_file('C:\\Users\zivsi\OneDrive\תמונות\סרט צילום\WIN_20191115_10_32_24_Pro.jpg') # my picture 1
b = face_recognition.load_image_file('C:\\Users\zivsi\OneDrive\תמונות\סרט צילום\WIN_20191115_09_48_56_Pro.jpg') # my picture 2
c = face_recognition.load_image_file(
'C:\\Users\zivsi\OneDrive\תמונות\סרט צילום\WIN_20191115_09_48_52_Pro.jpg') # my picture 3
d = face_recognition.load_image_file('C:\\Users\zivsi\OneDrive\תמונות\סרט צילום\ziv sion.jpg') # my picture 4
e = face_recognition.load_image_file(
'C:\\Users\zivsi\OneDrive\תמונות\סרט צילום\WIN_20191120_17_46_40_Pro.jpg') # my picture 5
f = face_recognition.load_image_file(
'C:\\Users\zivsi\OneDrive\תמונות\סרט צילום\WIN_20191117_16_19_11_Pro.jpg') # my picture 6
a = face_recognition.face_encodings(a)[0]
b = face_recognition.face_encodings(b)[0]
c = face_recognition.face_encodings(c)[0]
d = face_recognition.face_encodings(d)[0]
e = face_recognition.face_encodings(e)[0]
f = face_recognition.face_encodings(f)[0]
Here I tried to convert the variable to a string
str_variable = str(a)
array_variable = np.array(str_variable)
my_face = a, b, c, d, e, f, array_variable
while True:
new = input('path: ')
print('Recognizing...')
unknown = face_recognition.load_image_file(new)
unknown_encodings = face_recognition.face_encodings(unknown)[0]
The program cannot use the variable:
results = face_recognition.compare_faces(array_variable, unknown_encodings, tolerance=0.4)
print(results)
recognize_times = int(results.count(True))
if (3 <= recognize_times):
print('hello boss!')
my_face = *my_face, unknown_encodings
please help me
The error shown:
Traceback (most recent call last):
File "C:/Users/zivsi/PycharmProjects/AI/pytt.py", line 37, in <module>
results = face_recognition.compare_faces(my_face, unknown_encodings, tolerance=0.4)
File "C:\Users\zivsi\AppData\Local\Programs\Python\Python36\lib\site-
packages\face_recognition\api.py", line 222, in compare_faces
return list(face_distance(known_face_encodings, face_encoding_to_check) <= tolerance)
File "C:\Users\zivsi\AppData\Local\Programs\Python\Python36\lib\site-packages\face_recognition\api.py", line 72, in face_distance
return np.linalg.norm(face_encodings - face_to_compare, axis=1)
ValueError: operands could not be broadcast together with shapes (7,) (128,)
First of all, the array_variable should actually be a list of the known encodings and not a numpy array.
Also you do not need str.
Now, in your case, if the input images i.e., a,b,c,d,f,e do NOT have the same dimensions, the error will persist. You can not compare images that have different sizes using this function. The reason is that the comparison is based on the distance and distance is defined on vectors of the same length.
Here is a working simple example using the photos from https://github.com/ageitgey/face_recognition/tree/master/examples:
import face_recognition
import numpy as np
from PIL import Image, ImageDraw
from IPython.display import display
# Load a sample picture and learn how to recognize it.
obama_image = face_recognition.load_image_file("obama.jpg")
obama_face_encoding = face_recognition.face_encodings(obama_image)[0]
# Load a second sample picture and learn how to recognize it.
biden_image = face_recognition.load_image_file("biden.jpg")
biden_face_encoding = face_recognition.face_encodings(biden_image)[0]
array_variable = [obama_face_encoding,biden_face_encoding] # list of known encodings
# compare the list with the biden_face_encoding
results = face_recognition.compare_faces(array_variable, biden_face_encoding, tolerance=0.4)
print(results)
[False, True] # True means match, False mismatch
# False: coming from obama_face_encoding VS biden_face_encoding
# True: coming from biden_face_encoding VS biden_face_encoding
To run it go here: https://beta.deepnote.com/project/09705740-31c0-4d9a-8890-269ff1c3dfaf#
Documentation: https://face-recognition.readthedocs.io/en/latest/face_recognition.html
EDIT
To save the known encodings you can use numpy.save
np.save('encodings',biden_face_encoding) # save
load_again = np.load('encodings.npy') # load again
i'm trying to follow this tutorial with my own local data files:
CNTK tutorial
i have the following function to save my data array into a txt file feedable to CNTK:
# Save the data files into a format compatible with CNTK text reader
def savetxt(filename, ndarray):
dir = os.path.dirname(filename)
if not os.path.exists(dir):
os.makedirs(dir)
if not os.path.isfile(filename):
print("Saving", filename )
with open(filename, 'w') as f:
labels = list(map(' '.join, np.eye(11, dtype=np.uint).astype(str)))
for row in ndarray:
row_str = row.astype(str)
label_str = labels[row[-1]]
feature_str = ' '.join(row_str[:-1])
f.write('|labels {} |features {}\n'.format(label_str, feature_str))
else:
print("File already exists", filename)
i have 2 ndarrays of the following shape that i want to feed the model:
train.shape
(1976L, 15104L)
test.shape
(1976L, 15104L)
Then i try to implement the fucntion like this:
# Save the train and test files (prefer our default path for the data)
data_dir = os.path.join("C:/Users", 'myself', "OneDrive", "IA Project", 'data', 'train')
if not os.path.exists(data_dir):
data_dir = os.path.join("data", "IA Project")
print ('Writing train text file...')
savetxt(os.path.join(data_dir, "Train-128x118_cntk_text.txt"), train)
print ('Writing test text file...')
savetxt(os.path.join(data_dir, "Test-128x118_cntk_text.txt"), test)
print('Done')
and then i get the following error:
Writing train text file...
Saving C:/Users\A702628\OneDrive - Atos\Microsoft Capstone IA\Capstone data\train\Train-128x118_cntk_text.txt
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-24-b53d3c69b8d2> in <module>()
6
7 print ('Writing train text file...')
----> 8 savetxt(os.path.join(data_dir, "Train-128x118_cntk_text.txt"), train)
9
10 print ('Writing test text file...')
<ipython-input-23-610c077db694> in savetxt(filename, ndarray)
12 for row in ndarray:
13 row_str = row.astype(str)
---> 14 label_str = labels[row[-1]]
15 feature_str = ' '.join(row_str[:-1])
16 f.write('|labels {} |features {}\n'.format(label_str, feature_str))
IndexError: list index out of range
Can somebody please tell me what's going wrong with this part of the code? And how could i fix it? Thank you very much in advance.
Since you're using your own input data -- are they labelled in the range 0 to 9? The labels array only has 10 entries in it, so that could cause an out-of-range problem.
I use Anaconda and gdsCAD and get an error when all packages are installed correctly.
Like explained here: http://pythonhosted.org/gdsCAD/
TypeError: ufunc 'add' did not contain a loop with signature matching types dtype('S32') dtype('S32') dtype('S32')
My imports look like this (In the end I imported everything):
import numpy as np
from gdsCAD import *
import matplotlib.pyplot as plt
My example code looks like this:
something = core.Elements()
box=shapes.Box( (5,5),(1,5),0.5)
core.default_layer = 1
core.default_colors = 2
something.add(box)
something.show()
My error message looks like this:
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-5-2f90b960c1c1> in <module>()
31 puffer_wafer = shapes.Circle((0.,0.), puffer_wafer_radius, puffer_line_thickness)
32 bp.add(puffer_wafer)
---> 33 bp.show()
34 wafer = shapes.Circle((0.,0.), wafer_radius, wafer_line_thickness)
35 bp.add(wafer)
C:\Users\rpilz\AppData\Local\Continuum\Anaconda2\lib\site-packages\gdscad-0.4.5-py2.7.egg\gdsCAD\core.pyc in _show(self)
80 ax.margins(0.1)
81
---> 82 artists=self.artist()
83 for a in artists:
84 a.set_transform(a.get_transform() + ax.transData)
C:\Users\rpilz\AppData\Local\Continuum\Anaconda2\lib\site-packages\gdscad-0.4.5-py2.7.egg\gdsCAD\core.pyc in artist(self, color)
952 art=[]
953 for p in self:
--> 954 art+=p.artist()
955 return art
956
C:\Users\rpilz\AppData\Local\Continuum\Anaconda2\lib\site-packages\gdscad-0.4.5-py2.7.egg\gdsCAD\core.pyc in artist(self, color)
475 poly = lines.buffer(self.width/2.)
476
--> 477 return [descartes.PolygonPatch(poly, lw=0, **self._layer_properties(self.layer))]
478
479
C:\Users\rpilz\AppData\Local\Continuum\Anaconda2\lib\site-packages\gdscad-0.4.5-py2.7.egg\gdsCAD\core.pyc in _layer_properties(layer)
103 # Default colors from previous versions
104 colors = ['k', 'r', 'g', 'b', 'c', 'm', 'y']
--> 105 colors += matplotlib.cm.gist_ncar(np.linspace(0.98, 0, 15))
106 color = colors[layer % len(colors)]
107 return {'color': color}
TypeError: ufunc 'add' did not contain a loop with signature matching types dtype('S32') dtype('S32') dtype('S32')
The gdsCAD has been a pain from shapely install to this plotting issue.
This issue is because of wrong datatype being passed to colors function. It can be solved by editing the following line in core.py
colors += matplotlib.cm.gist_ncar(np.linspace(0.98, 0, 15))
to
colors += list(matplotlib.cm.gist_ncar(np.linspace(0.98, 0, 15)))
If you dont know where the core.py is located. Just type in:
from gdsCAD import *
core
This will give you the path of core.py file. Good luck !
Well first, I'd ask that you please include actual code, as the 'example code' in the file is obviously different based on the traceback. When debugging, the details matter, and I need to be able to actually run the code.
You obviously have a data type problem. Chances are pretty good it's in the variables here:
puffer_wafer = shapes.Circle((0.,0.), puffer_wafer_radius, puffer_line_thickness)
I had the same error thrown when I was running a call to Pandas. I changed the data to str(data) and the code worked.
I don't know if this helps I am fairly new to this myself, but I had a similar error and found that it is due to a type casting issue as suggested by previous answer. I can't see from the example in the question exactly what you are trying to do. Below is a small example of my issue and solution. My code is making a simple Random Forest model using scikit learn.
Here is an example that will give the error and it is caused by the third to last line, concatenating the results to write to file.
import scipy
import math
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn import preprocessing, metrics, cross_validation
Data = pd.read_csv("Free_Energy_exp.csv", sep=",")
Data = Data.fillna(Data.mean()) # replace the NA values with the mean of the descriptor
header = Data.columns.values # Ues the column headers as the descriptor labels
Data.head()
test_name = "Test.csv"
npArray = np.array(Data)
print header.shape
npheader = np.array(header[1:-1])
print("Array shape X = %d, Y = %d " % (npArray.shape))
datax, datay = npArray.shape
names = npArray[:,0]
X = npArray[:,1:-1].astype(float)
y = npArray[:,-1] .astype(float)
X = preprocessing.scale(X)
XTrain, XTest, yTrain, yTest = cross_validation.train_test_split(X,y, random_state=0)
# Predictions results initialised
RFpredictions = []
RF = RandomForestRegressor(n_estimators = 10, max_features = 5, max_depth = 5, random_state=0)
RF.fit(XTrain, yTrain) # Train the model
print("Training R2 = %5.2f" % RF.score(XTrain,yTrain))
RFpreds = RF.predict(XTest)
with open(test_name,'a') as fpred :
lenpredictions = len(RFpreds)
lentrue = yTest.shape[0]
if lenpredictions == lentrue :
fpred.write("Names/Label,, Prediction Random Forest,, True Value,\n")
for i in range(0,lenpredictions) :
fpred.write(RFpreds[i]+",,"+yTest[i]+",\n")
else :
print "ERROR - names, prediction and true value array size mismatch."
This leads to an error of;
Traceback (most recent call last):
File "min_example.py", line 40, in <module>
fpred.write(RFpreds[i]+",,"+yTest[i]+",\n")
TypeError: ufunc 'add' did not contain a loop with signature matching types dtype('S32') dtype('S32') dtype('S32')
The solution is to make each variable a str() type on the third to last line then write to file. No other changes to then code have been made from the above.
import scipy
import math
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn import preprocessing, metrics, cross_validation
Data = pd.read_csv("Free_Energy_exp.csv", sep=",")
Data = Data.fillna(Data.mean()) # replace the NA values with the mean of the descriptor
header = Data.columns.values # Ues the column headers as the descriptor labels
Data.head()
test_name = "Test.csv"
npArray = np.array(Data)
print header.shape
npheader = np.array(header[1:-1])
print("Array shape X = %d, Y = %d " % (npArray.shape))
datax, datay = npArray.shape
names = npArray[:,0]
X = npArray[:,1:-1].astype(float)
y = npArray[:,-1] .astype(float)
X = preprocessing.scale(X)
XTrain, XTest, yTrain, yTest = cross_validation.train_test_split(X,y, random_state=0)
# Predictions results initialised
RFpredictions = []
RF = RandomForestRegressor(n_estimators = 10, max_features = 5, max_depth = 5, random_state=0)
RF.fit(XTrain, yTrain) # Train the model
print("Training R2 = %5.2f" % RF.score(XTrain,yTrain))
RFpreds = RF.predict(XTest)
with open(test_name,'a') as fpred :
lenpredictions = len(RFpreds)
lentrue = yTest.shape[0]
if lenpredictions == lentrue :
fpred.write("Names/Label,, Prediction Random Forest,, True Value,\n")
for i in range(0,lenpredictions) :
fpred.write(str(RFpreds[i])+",,"+str(yTest[i])+",\n")
else :
print "ERROR - names, prediction and true value array size mismatch."
These examples are from a larger code so I hope the examples are clear enough.
This question already has answers here:
Issue querying from Access database: "could not convert string to float: E+6"
(3 answers)
Closed 7 years ago.
I am trying to access some of the columns in a Microsoft Access database table which contains numbers of type double but I am getting the error mentioned in the title.The code used for querying the database is as below, the error is occurring in the line where cur.execute(....) command is executed. Basically I am trying filter out data captured in a particular time interval. If I exclude the columns CM3_Up, CG3_Up, CM3_Down, CG3_Down which contains double data type in the cur.execute(....) command I wont get the error. Same logic was used to access double data type from other tables and it worked fine, I am not sure what is going wrong.
Code:
start =datetime.datetime(2015,03,28,00,00)
a=start
b=start+datetime.timedelta(0,240)
r=7
while a < (start+datetime.timedelta(1)):
params = (a,b)
sql = "SELECT Date_Time, CM3_Up, CG3_Up, CM3_Down, CG3_Down FROM
Lysimeter_Facility_Data_5 WHERE Date_Time >= ? AND Date_Time <= ?"
for row in cur.execute(sql,params):
if row is None:
continue
r = r+1
ws.cell(row = r,column=12).value = row.get('CM3_Up')
ws.cell(row = r,column=13).value = row.get('CG3_Up')
ws.cell(row = r,column=14).value = row.get('CM3_Down')
ws.cell(row = r,column=15).value = row.get('CG3_Down')
a = a+five_min
b = b+five_min
wb.save('..\SE_SW_Lysimeters_Weather_Mass_Experiment-02_03_26_2015.xlsx')
Complete error report:
Traceback (most recent call last):
File "C:\DB_PY\access_mdb\db_to_xl.py", line 318, in <module>
for row in cur.execute(sql,params):
File "build\bdist.win32\egg\pypyodbc.py", line 1920, in next
row = self.fetchone()
File "build\bdist.win32\egg\pypyodbc.py", line 1871, in fetchone
value_list.append(buf_cvt_func(alloc_buffer.value))
ValueError: could not convert string to float: E-3
As to this discussion:
Python: trouble reading number format
the trouble could be that e should be d, like:
float(row.get('CM3_Up').replace('E', 'D'))
Sounds weird to me though, but I know only little of Python.
It sounds like you receive strings like '2.34E-3', so try with a conversion. Don't know Python, but in C# it could be like:
ws.cell(row = r,column=12).value = Convert.ToDouble(row.get('CM3_Up'))
ws.cell(row = r,column=13).value = Convert.ToDouble(row.get('CG3_Up'))
ws.cell(row = r,column=14).value = Convert.ToDouble(row.get('CM3_Down'))
ws.cell(row = r,column=15).value = Convert.ToDouble(row.get('CG3_Down'))
i have many data.frames() that i am trying to send to MySQL database via RMySQL().
# Sends data frame to database without a problem
dbWriteTable(con3, name="SPY", value=SPY , append=T)
# stock1 contains a character vector of stock names...
stock1 <- c("SPY.A")
But when I try to loop it:
i= 1
while(i <= length(stock1)){
# converts "SPY.A" into SPY
name <- print(paste0(str_sub(stock1, start = 1, end = -3))[i], quote=F)
# sends data.frame to database
dbWriteTable(con3,paste0(str_sub(stock1, start = 1, end = -3))[i], value=name, append=T)
i <- 1+i
}
The following warning is returned & nothing was sent to database
In addition: Warning message:
In file(fn, open = "r") :
cannot open file './SPY': No such file or directory
However, I believe that the problem is with pasting value onto dbWriteTable() since writing dbWriteTable(con3, "SPY", SPY, append=T) works but dbWriteTable(con3, "SPY", name, append=T) will not...
You are probably using a non-base package for str_sub and I'm guessing you get the same behavior with substr. Does this succeed?
dbWriteTable(con3, substr( stock1, 1,3) , get(stock1), append=T)