Non positive semi-definiteness error when creating time series in Python - python

I am receiving an error when I try to fit some data I have to a VAR Time series model in Python via statsmodels, the documentation of which is available here:
The data I have is available in a dataframe df_IBM_training which can be seen below:
date sym open high low close newscount
6 2014.08.05 IBM 189.30 189.3000 186.4100 187.0800 4
9 2014.08.06 IBM 185.80 186.8800 184.4400 185.9000 0
12 2014.08.07 IBM 186.56 186.8800 1.0000 184.2800 2
15 2014.08.08 IBM 183.32 186.6800 183.3200 186.5499 18
The model VAR I want to build looks like this, the regressors of which I try to create in the code below. I also try to search for the ideal model order in the code below, which is where I get the error. Each coefficient in the equation below, examples include α1,1, γ1,11 is associated with a regressor in the code:
Δlog(C_t) = α1,1(log(C_t - 1) − log(O_t-1))
+ α1,2(log‌​(C_t - 1) − log(H_t-1))
+ α1,3(log(C_t - 1) − log(L_t-1))
+ γ1,11Δlog(C_t − 1)
+ γ1,12Δlog(O_t − 1)
+ γ1,13Δlog(H_t − 1)
+ γ1,14Δlog(L_t − 1)
+ εt
My code is as follows. For some reason, I get the following error in the line model.select_order(8):
numpy.linalg.linalg.linalgerror 7-th leading minor not positive semi-definite
#VAR regressors
df_IBM_training['log_ret0'] = np.log(df_IBM_training.close) - np.log(df_IBM_training.close.shift(1))
df_IBM_training['log_ret1'] = np.log(df_IBM_training.open) - np.log(df_IBM_training.open.shift(1))
df_IBM_training['log_ret2'] = np.log(df_IBM_training.high) - np.log(df_IBM_training.high.shift(1))
df_IBM_training['log_ret3'] = np.log(df_IBM_training.low) - np.log(df_IBM_training.low.shift(1))
df_IBM_training = df_IBM_training[np.isfinite(df_IBM_training['log_ret3'])]
regressor_1 = np.log(df_IBM_training['close']) - np.log(df_IBM_training['open'])
regressor_2 = np.log(df_IBM_training['close']) - np.log(df_IBM_training['high'])
regressor_3 = np.log(df_IBM_training['close']) - np.log(df_IBM_training['low'])
regressor_4 = df_IBM_training['log_ret0']
regressor_5 = df_IBM_training['log_ret1']
regressor_6 = df_IBM_training['log_ret2']
regressor_7 = df_IBM_training['log_ret3']
X_IBM = [regressor_1, regressor_2, regressor_3, regressor_4,regressor_5, regressor_6, regressor_7]
X_IBM = np.array(X_IBM)
X_IBM = X_IBM.T
model = statsmodels.tsa.api.VAR(X_IBM)
#The line below is where the error arises
model.select_order(8)
Edit: Traceback Error below:
Traceback (most recent call last):
File "TimeSeries.py", line 70, in <module>
model.select_order(8)
File "C:\Python34\lib\site-packages\statsmodels\tsa\vector_ar\var_model.py", line 505, in select_order
for k, v in iteritems(result.info_criteria):
File "C:\Python34\lib\site-packages\statsmodels\base\wrapper.py", line 35, in __getattribute__
obj = getattr(results, attr)
File "C:\Python34\lib\site-packages\statsmodels\tools\decorators.py", line 94, in __get__
_cachedval = self.fget(obj)
File "C:\Python34\lib\site-packages\statsmodels\tsa\vector_ar\var_model.py", line 1468, in info_criteria
ld = logdet_symm(self.sigma_u_mle)
File "C:\Python34\lib\site-packages\statsmodels\tools\linalg.py", line 213, in logdet_symm
c, _ = linalg.cho_factor(m, lower=True)
File "C:\Python34\lib\site-packages\scipy\linalg\decomp_cholesky.py", line 132, in cho_factor
check_finite=check_finite)
File "C:\Python34\lib\site-packages\scipy\linalg\decomp_cholesky.py", line 30, in _cholesky
raise LinAlgError("%d-th leading minor not positive definite" % info)
numpy.linalg.linalg.LinAlgError: 5-th leading minor not positive definite

Related

TypeError when fitting Statsmodels OLS with standard errors clustered 2 ways

Context
Building on top of How to run Panel OLS regressions with 3+ fixed-effect and errors clustering? and notably Josef's third comment, I am trying to adapt the OLS Coefficients and Standard Errors Clustered by Firm and Year section of this example notebook below:
cluster_2ways_ols = sm.ols(formula='y ~ x', data=df).fit(cov_type='cluster',
cov_kwds={'groups': np.array(df[['firmid', 'year']])},
use_t=True)
to my own example dataset.
Note that I am able to reproduce this example (and it works). I can also add fixed-effects, by using 'y ~ x + C(firmid) + C(year)' as formula instead.
Problem
However, trying to port the same command to my example dataset (see code below), I'm getting the following error:
>>> model = sm.OLS.from_formula("gdp ~ population + C(year_publication) + C(country)", df)
>>> result = model.fit(
cov_type='cluster',
cov_kwds={'groups': np.array(df[['country', 'year_publication']])},
use_t=True
)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/path/venv/lib64/python3.10/site-packages/statsmodels/regression/linear_model.py", line 343, in fit
lfit = OLSResults(
File "/path/venv/lib64/python3.10/site-packages/statsmodels/regression/linear_model.py", line 1607, in __init__
self.get_robustcov_results(cov_type=cov_type, use_self=True,
File "/path/venv/lib64/python3.10/site-packages/statsmodels/regression/linear_model.py", line 2568, in get_robustcov_results
res.cov_params_default = sw.cov_cluster_2groups(
File "/path/venv/lib64/python3.10/site-packages/statsmodels/stats/sandwich_covariance.py", line 591, in cov_cluster_2groups
combine_indices(group)[0],
File "/path/venv/lib64/python3.10/site-packages/statsmodels/tools/grouputils.py", line 55, in combine_indices
groups_ = groups.view([('', groups.dtype)] * groups.shape[1])
File "/path/venv/lib64/python3.10/site-packages/numpy/core/_internal.py", line 549, in _view_is_safe
raise TypeError("Cannot change data-type for object array.")
TypeError: Cannot change data-type for object array.
I have tried to manually cast the year_publication to string/object using np.array(df[['country', 'year_publication']].astype("str")), but it doesn't solve the issue.
Questions
What is the cause of the TypeError()?
How to adapt the example command to my dataset?
Minimal Working Example
from io import StringIO
import numpy as np
import pandas as pd
import statsmodels.api as sm
DATA = """
"continent","country","source","year_publication","year_data","population","gdp"
"Africa","Angola","OECD",2020,2018,972,52.69
"Africa","Angola","OECD",2020,2019,986,802.7
"Africa","Angola","OECD",2020,2020,641,568.74
"Africa","Angola","OECD",2021,2018,438,168.83
"Africa","Angola","OECD",2021,2019,958,310.57
"Africa","Angola","OECD",2021,2020,270,144.02
"Africa","Angola","OECD",2022,2018,528,359.71
"Africa","Angola","OECD",2022,2019,974,582.98
"Africa","Angola","OECD",2022,2020,835,820.49
"Africa","Angola","IMF",2020,2018,168,148.85
"Africa","Angola","IMF",2020,2019,460,236.21
"Africa","Angola","IMF",2020,2020,360,297.15
"Africa","Angola","IMF",2021,2018,381,249.13
"Africa","Angola","IMF",2021,2019,648,128.05
"Africa","Angola","IMF",2021,2020,206,179.05
"Africa","Angola","IMF",2022,2018,282,150.29
"Africa","Angola","IMF",2022,2019,125,23.42
"Africa","Angola","IMF",2022,2020,410,247.35
"Africa","Angola","WorldBank",2020,2018,553,182.06
"Africa","Angola","WorldBank",2020,2019,847,698.87
"Africa","Angola","WorldBank",2020,2020,844,126.61
"Africa","Angola","WorldBank",2021,2018,307,239.76
"Africa","Angola","WorldBank",2021,2019,659,510.73
"Africa","Angola","WorldBank",2021,2020,548,331.89
"Africa","Angola","WorldBank",2022,2018,448,122.76
"Africa","Angola","WorldBank",2022,2019,768,761.41
"Africa","Angola","WorldBank",2022,2020,324,163.57
"Africa","Benin","OECD",2020,2018,513,196.9
"Africa","Benin","OECD",2020,2019,590,83.7
"Africa","Benin","OECD",2020,2020,791,511.09
"Africa","Benin","OECD",2021,2018,799,474.43
"Africa","Benin","OECD",2021,2019,455,234.21
"Africa","Benin","OECD",2021,2020,549,238.83
"Africa","Benin","OECD",2022,2018,235,229.33
"Africa","Benin","OECD",2022,2019,347,46.51
"Africa","Benin","OECD",2022,2020,532,392.13
"Africa","Benin","IMF",2020,2018,138,137.05
"Africa","Benin","IMF",2020,2019,978,239.82
"Africa","Benin","IMF",2020,2020,821,33.41
"Africa","Benin","IMF",2021,2018,453,291.93
"Africa","Benin","IMF",2021,2019,526,381.88
"Africa","Benin","IMF",2021,2020,467,313.57
"Africa","Benin","IMF",2022,2018,948,555.23
"Africa","Benin","IMF",2022,2019,323,289.91
"Africa","Benin","IMF",2022,2020,421,62.35
"Africa","Benin","WorldBank",2020,2018,983,271.69
"Africa","Benin","WorldBank",2020,2019,138,23.55
"Africa","Benin","WorldBank",2020,2020,636,623.65
"Africa","Benin","WorldBank",2021,2018,653,534.99
"Africa","Benin","WorldBank",2021,2019,564,368.8
"Africa","Benin","WorldBank",2021,2020,741,312.02
"Africa","Benin","WorldBank",2022,2018,328,292.11
"Africa","Benin","WorldBank",2022,2019,653,429.21
"Africa","Benin","WorldBank",2022,2020,951,242.73
"Africa","Chad","OECD",2020,2018,176,95.06
"Africa","Chad","OECD",2020,2019,783,425.34
"Africa","Chad","OECD",2020,2020,885,461.6
"Africa","Chad","OECD",2021,2018,673,15.87
"Africa","Chad","OECD",2021,2019,131,74.46
"Africa","Chad","OECD",2021,2020,430,61.58
"Africa","Chad","OECD",2022,2018,593,211.34
"Africa","Chad","OECD",2022,2019,647,550.37
"Africa","Chad","OECD",2022,2020,154,105.65
"Africa","Chad","IMF",2020,2018,160,32.41
"Africa","Chad","IMF",2020,2019,654,27.84
"Africa","Chad","IMF",2020,2020,616,468.92
"Africa","Chad","IMF",2021,2018,996,22.4
"Africa","Chad","IMF",2021,2019,126,93.18
"Africa","Chad","IMF",2021,2020,879,547.87
"Africa","Chad","IMF",2022,2018,663,520
"Africa","Chad","IMF",2022,2019,681,544.76
"Africa","Chad","IMF",2022,2020,101,55.6
"Africa","Chad","WorldBank",2020,2018,786,757.22
"Africa","Chad","WorldBank",2020,2019,599,593.69
"Africa","Chad","WorldBank",2020,2020,641,529.84
"Africa","Chad","WorldBank",2021,2018,343,287.89
"Africa","Chad","WorldBank",2021,2019,438,340.83
"Africa","Chad","WorldBank",2021,2020,762,594.67
"Africa","Chad","WorldBank",2022,2018,430,128.69
"Africa","Chad","WorldBank",2022,2019,260,242.59
"Africa","Chad","WorldBank",2022,2020,607,216.1
"Europe","Denmark","OECD",2020,2018,114,86.75
"Europe","Denmark","OECD",2020,2019,937,373.29
"Europe","Denmark","OECD",2020,2020,866,392.93
"Europe","Denmark","OECD",2021,2018,296,41.04
"Europe","Denmark","OECD",2021,2019,402,32.67
"Europe","Denmark","OECD",2021,2020,306,7.88
"Europe","Denmark","OECD",2022,2018,540,379.51
"Europe","Denmark","OECD",2022,2019,108,26.72
"Europe","Denmark","OECD",2022,2020,752,307.2
"Europe","Denmark","IMF",2020,2018,157,24.24
"Europe","Denmark","IMF",2020,2019,303,79.04
"Europe","Denmark","IMF",2020,2020,286,122.36
"Europe","Denmark","IMF",2021,2018,569,69.32
"Europe","Denmark","IMF",2021,2019,808,642.67
"Europe","Denmark","IMF",2021,2020,157,5.58
"Europe","Denmark","IMF",2022,2018,147,112.21
"Europe","Denmark","IMF",2022,2019,414,311.16
"Europe","Denmark","IMF",2022,2020,774,230.46
"Europe","Denmark","WorldBank",2020,2018,695,350.03
"Europe","Denmark","WorldBank",2020,2019,511,209.84
"Europe","Denmark","WorldBank",2020,2020,181,29.27
"Europe","Denmark","WorldBank",2021,2018,503,176.89
"Europe","Denmark","WorldBank",2021,2019,710,609.02
"Europe","Denmark","WorldBank",2021,2020,264,165.78
"Europe","Denmark","WorldBank",2022,2018,670,638.99
"Europe","Denmark","WorldBank",2022,2019,651,354.6
"Europe","Denmark","WorldBank",2022,2020,632,623.94
"Europe","Estonia","OECD",2020,2018,838,263.67
"Europe","Estonia","OECD",2020,2019,638,533.95
"Europe","Estonia","OECD",2020,2020,898,638.73
"Europe","Estonia","OECD",2021,2018,262,98.16
"Europe","Estonia","OECD",2021,2019,569,552.54
"Europe","Estonia","OECD",2021,2020,868,252.48
"Europe","Estonia","OECD",2022,2018,927,264.65
"Europe","Estonia","OECD",2022,2019,205,150.6
"Europe","Estonia","OECD",2022,2020,828,752.61
"Europe","Estonia","IMF",2020,2018,841,176.31
"Europe","Estonia","IMF",2020,2019,614,230.55
"Europe","Estonia","IMF",2020,2020,500,41.19
"Europe","Estonia","IMF",2021,2018,510,169.68
"Europe","Estonia","IMF",2021,2019,765,401.85
"Europe","Estonia","IMF",2021,2020,751,319.6
"Europe","Estonia","IMF",2022,2018,314,58.81
"Europe","Estonia","IMF",2022,2019,155,2.24
"Europe","Estonia","IMF",2022,2020,734,187.6
"Europe","Estonia","WorldBank",2020,2018,332,160.17
"Europe","Estonia","WorldBank",2020,2019,466,385.33
"Europe","Estonia","WorldBank",2020,2020,487,435.06
"Europe","Estonia","WorldBank",2021,2018,461,249.19
"Europe","Estonia","WorldBank",2021,2019,932,763.38
"Europe","Estonia","WorldBank",2021,2020,650,463.91
"Europe","Estonia","WorldBank",2022,2018,570,549.97
"Europe","Estonia","WorldBank",2022,2019,909,80.48
"Europe","Estonia","WorldBank",2022,2020,523,242.22
"Europe","Finland","OECD",2020,2018,565,561.64
"Europe","Finland","OECD",2020,2019,646,161.62
"Europe","Finland","OECD",2020,2020,194,133.69
"Europe","Finland","OECD",2021,2018,529,39.76
"Europe","Finland","OECD",2021,2019,800,680.12
"Europe","Finland","OECD",2021,2020,418,399.19
"Europe","Finland","OECD",2022,2018,591,253.12
"Europe","Finland","OECD",2022,2019,457,272.58
"Europe","Finland","OECD",2022,2020,157,105.1
"Europe","Finland","IMF",2020,2018,860,445.03
"Europe","Finland","IMF",2020,2019,108,47.72
"Europe","Finland","IMF",2020,2020,523,500.58
"Europe","Finland","IMF",2021,2018,560,81.47
"Europe","Finland","IMF",2021,2019,830,664.64
"Europe","Finland","IMF",2021,2020,903,762.62
"Europe","Finland","IMF",2022,2018,179,167.73
"Europe","Finland","IMF",2022,2019,137,98.98
"Europe","Finland","IMF",2022,2020,666,524.86
"Europe","Finland","WorldBank",2020,2018,319,146.01
"Europe","Finland","WorldBank",2020,2019,401,219.56
"Europe","Finland","WorldBank",2020,2020,711,45.35
"Europe","Finland","WorldBank",2021,2018,828,20.97
"Europe","Finland","WorldBank",2021,2019,180,66.3
"Europe","Finland","WorldBank",2021,2020,682,92.57
"Europe","Finland","WorldBank",2022,2018,254,81.2
"Europe","Finland","WorldBank",2022,2019,619,159.08
"Europe","Finland","WorldBank",2022,2020,191,184.4
"""
df = pd.read_csv(StringIO(DATA))
model = sm.OLS.from_formula("gdp ~ population + C(year_publication) + C(country)", df)
result = model.fit(
cov_type='cluster',
cov_kwds={'groups': np.array(df[['country', 'year_publication']])},
use_t=True
)
print(result.summary())

I have realized that the groups must be an array of integers rather than of objects/strings.
Thus, label encoding the string column as follows:
df["country"] = df["country"].astype("category")
df["country_id"] = df.country.cat.codes
and using country_id to cluster the standard errors solves the issue:
result = model.fit(
cov_type='cluster',
cov_kwds={'groups': np.array(df[['country_id', 'year_publication']])},
use_t=True
)
Fully working example:
from io import StringIO
import numpy as np
import pandas as pd
import statsmodels.api as sm
DATA = """
"continent","country","source","year_publication","year_data","population","gdp"
"Africa","Angola","OECD",2020,2018,972,52.69
"Africa","Angola","OECD",2020,2019,986,802.7
"Africa","Angola","OECD",2020,2020,641,568.74
"Africa","Angola","OECD",2021,2018,438,168.83
"Africa","Angola","OECD",2021,2019,958,310.57
"Africa","Angola","OECD",2021,2020,270,144.02
"Africa","Angola","OECD",2022,2018,528,359.71
"Africa","Angola","OECD",2022,2019,974,582.98
"Africa","Angola","OECD",2022,2020,835,820.49
"Africa","Angola","IMF",2020,2018,168,148.85
"Africa","Angola","IMF",2020,2019,460,236.21
"Africa","Angola","IMF",2020,2020,360,297.15
"Africa","Angola","IMF",2021,2018,381,249.13
"Africa","Angola","IMF",2021,2019,648,128.05
"Africa","Angola","IMF",2021,2020,206,179.05
"Africa","Angola","IMF",2022,2018,282,150.29
"Africa","Angola","IMF",2022,2019,125,23.42
"Africa","Angola","IMF",2022,2020,410,247.35
"Africa","Angola","WorldBank",2020,2018,553,182.06
"Africa","Angola","WorldBank",2020,2019,847,698.87
"Africa","Angola","WorldBank",2020,2020,844,126.61
"Africa","Angola","WorldBank",2021,2018,307,239.76
"Africa","Angola","WorldBank",2021,2019,659,510.73
"Africa","Angola","WorldBank",2021,2020,548,331.89
"Africa","Angola","WorldBank",2022,2018,448,122.76
"Africa","Angola","WorldBank",2022,2019,768,761.41
"Africa","Angola","WorldBank",2022,2020,324,163.57
"Africa","Benin","OECD",2020,2018,513,196.9
"Africa","Benin","OECD",2020,2019,590,83.7
"Africa","Benin","OECD",2020,2020,791,511.09
"Africa","Benin","OECD",2021,2018,799,474.43
"Africa","Benin","OECD",2021,2019,455,234.21
"Africa","Benin","OECD",2021,2020,549,238.83
"Africa","Benin","OECD",2022,2018,235,229.33
"Africa","Benin","OECD",2022,2019,347,46.51
"Africa","Benin","OECD",2022,2020,532,392.13
"Africa","Benin","IMF",2020,2018,138,137.05
"Africa","Benin","IMF",2020,2019,978,239.82
"Africa","Benin","IMF",2020,2020,821,33.41
"Africa","Benin","IMF",2021,2018,453,291.93
"Africa","Benin","IMF",2021,2019,526,381.88
"Africa","Benin","IMF",2021,2020,467,313.57
"Africa","Benin","IMF",2022,2018,948,555.23
"Africa","Benin","IMF",2022,2019,323,289.91
"Africa","Benin","IMF",2022,2020,421,62.35
"Africa","Benin","WorldBank",2020,2018,983,271.69
"Africa","Benin","WorldBank",2020,2019,138,23.55
"Africa","Benin","WorldBank",2020,2020,636,623.65
"Africa","Benin","WorldBank",2021,2018,653,534.99
"Africa","Benin","WorldBank",2021,2019,564,368.8
"Africa","Benin","WorldBank",2021,2020,741,312.02
"Africa","Benin","WorldBank",2022,2018,328,292.11
"Africa","Benin","WorldBank",2022,2019,653,429.21
"Africa","Benin","WorldBank",2022,2020,951,242.73
"Africa","Chad","OECD",2020,2018,176,95.06
"Africa","Chad","OECD",2020,2019,783,425.34
"Africa","Chad","OECD",2020,2020,885,461.6
"Africa","Chad","OECD",2021,2018,673,15.87
"Africa","Chad","OECD",2021,2019,131,74.46
"Africa","Chad","OECD",2021,2020,430,61.58
"Africa","Chad","OECD",2022,2018,593,211.34
"Africa","Chad","OECD",2022,2019,647,550.37
"Africa","Chad","OECD",2022,2020,154,105.65
"Africa","Chad","IMF",2020,2018,160,32.41
"Africa","Chad","IMF",2020,2019,654,27.84
"Africa","Chad","IMF",2020,2020,616,468.92
"Africa","Chad","IMF",2021,2018,996,22.4
"Africa","Chad","IMF",2021,2019,126,93.18
"Africa","Chad","IMF",2021,2020,879,547.87
"Africa","Chad","IMF",2022,2018,663,520
"Africa","Chad","IMF",2022,2019,681,544.76
"Africa","Chad","IMF",2022,2020,101,55.6
"Africa","Chad","WorldBank",2020,2018,786,757.22
"Africa","Chad","WorldBank",2020,2019,599,593.69
"Africa","Chad","WorldBank",2020,2020,641,529.84
"Africa","Chad","WorldBank",2021,2018,343,287.89
"Africa","Chad","WorldBank",2021,2019,438,340.83
"Africa","Chad","WorldBank",2021,2020,762,594.67
"Africa","Chad","WorldBank",2022,2018,430,128.69
"Africa","Chad","WorldBank",2022,2019,260,242.59
"Africa","Chad","WorldBank",2022,2020,607,216.1
"Europe","Denmark","OECD",2020,2018,114,86.75
"Europe","Denmark","OECD",2020,2019,937,373.29
"Europe","Denmark","OECD",2020,2020,866,392.93
"Europe","Denmark","OECD",2021,2018,296,41.04
"Europe","Denmark","OECD",2021,2019,402,32.67
"Europe","Denmark","OECD",2021,2020,306,7.88
"Europe","Denmark","OECD",2022,2018,540,379.51
"Europe","Denmark","OECD",2022,2019,108,26.72
"Europe","Denmark","OECD",2022,2020,752,307.2
"Europe","Denmark","IMF",2020,2018,157,24.24
"Europe","Denmark","IMF",2020,2019,303,79.04
"Europe","Denmark","IMF",2020,2020,286,122.36
"Europe","Denmark","IMF",2021,2018,569,69.32
"Europe","Denmark","IMF",2021,2019,808,642.67
"Europe","Denmark","IMF",2021,2020,157,5.58
"Europe","Denmark","IMF",2022,2018,147,112.21
"Europe","Denmark","IMF",2022,2019,414,311.16
"Europe","Denmark","IMF",2022,2020,774,230.46
"Europe","Denmark","WorldBank",2020,2018,695,350.03
"Europe","Denmark","WorldBank",2020,2019,511,209.84
"Europe","Denmark","WorldBank",2020,2020,181,29.27
"Europe","Denmark","WorldBank",2021,2018,503,176.89
"Europe","Denmark","WorldBank",2021,2019,710,609.02
"Europe","Denmark","WorldBank",2021,2020,264,165.78
"Europe","Denmark","WorldBank",2022,2018,670,638.99
"Europe","Denmark","WorldBank",2022,2019,651,354.6
"Europe","Denmark","WorldBank",2022,2020,632,623.94
"Europe","Estonia","OECD",2020,2018,838,263.67
"Europe","Estonia","OECD",2020,2019,638,533.95
"Europe","Estonia","OECD",2020,2020,898,638.73
"Europe","Estonia","OECD",2021,2018,262,98.16
"Europe","Estonia","OECD",2021,2019,569,552.54
"Europe","Estonia","OECD",2021,2020,868,252.48
"Europe","Estonia","OECD",2022,2018,927,264.65
"Europe","Estonia","OECD",2022,2019,205,150.6
"Europe","Estonia","OECD",2022,2020,828,752.61
"Europe","Estonia","IMF",2020,2018,841,176.31
"Europe","Estonia","IMF",2020,2019,614,230.55
"Europe","Estonia","IMF",2020,2020,500,41.19
"Europe","Estonia","IMF",2021,2018,510,169.68
"Europe","Estonia","IMF",2021,2019,765,401.85
"Europe","Estonia","IMF",2021,2020,751,319.6
"Europe","Estonia","IMF",2022,2018,314,58.81
"Europe","Estonia","IMF",2022,2019,155,2.24
"Europe","Estonia","IMF",2022,2020,734,187.6
"Europe","Estonia","WorldBank",2020,2018,332,160.17
"Europe","Estonia","WorldBank",2020,2019,466,385.33
"Europe","Estonia","WorldBank",2020,2020,487,435.06
"Europe","Estonia","WorldBank",2021,2018,461,249.19
"Europe","Estonia","WorldBank",2021,2019,932,763.38
"Europe","Estonia","WorldBank",2021,2020,650,463.91
"Europe","Estonia","WorldBank",2022,2018,570,549.97
"Europe","Estonia","WorldBank",2022,2019,909,80.48
"Europe","Estonia","WorldBank",2022,2020,523,242.22
"Europe","Finland","OECD",2020,2018,565,561.64
"Europe","Finland","OECD",2020,2019,646,161.62
"Europe","Finland","OECD",2020,2020,194,133.69
"Europe","Finland","OECD",2021,2018,529,39.76
"Europe","Finland","OECD",2021,2019,800,680.12
"Europe","Finland","OECD",2021,2020,418,399.19
"Europe","Finland","OECD",2022,2018,591,253.12
"Europe","Finland","OECD",2022,2019,457,272.58
"Europe","Finland","OECD",2022,2020,157,105.1
"Europe","Finland","IMF",2020,2018,860,445.03
"Europe","Finland","IMF",2020,2019,108,47.72
"Europe","Finland","IMF",2020,2020,523,500.58
"Europe","Finland","IMF",2021,2018,560,81.47
"Europe","Finland","IMF",2021,2019,830,664.64
"Europe","Finland","IMF",2021,2020,903,762.62
"Europe","Finland","IMF",2022,2018,179,167.73
"Europe","Finland","IMF",2022,2019,137,98.98
"Europe","Finland","IMF",2022,2020,666,524.86
"Europe","Finland","WorldBank",2020,2018,319,146.01
"Europe","Finland","WorldBank",2020,2019,401,219.56
"Europe","Finland","WorldBank",2020,2020,711,45.35
"Europe","Finland","WorldBank",2021,2018,828,20.97
"Europe","Finland","WorldBank",2021,2019,180,66.3
"Europe","Finland","WorldBank",2021,2020,682,92.57
"Europe","Finland","WorldBank",2022,2018,254,81.2
"Europe","Finland","WorldBank",2022,2019,619,159.08
"Europe","Finland","WorldBank",2022,2020,191,184.4
"""
df = pd.read_csv(StringIO(DATA))
df["country"] = df["country"].astype("category")
df["country_id"] = df.country.cat.codes
model = sm.OLS.from_formula("gdp ~ population + C(year_publication) + C(country)", df)
result = model.fit(
cov_type='cluster',
cov_kwds={'groups': np.array(df[['country_id', 'year_publication']])},
use_t=True
)
print(result.summary())

Cmd module not cooperating with *args

I am trying to develop my Binance client with the Cmd module, however when I try to call a function from another file, it gives me an error.
This is for a Binance client i am writing.
main.py
def do_order(self, *args):
"""Places an order.\n
Arguments in order:\n
Buy or Sell option.\n
Market or Limit option.\n
The crypto symbol.\n
The quantity of the order.\n
The price of the order.\n"""
market.Market.xch(*args[0], *args[1], *args[2], *args[3], *args[4])
market.py
class Market():
#l = 0
#m = 0
#b = 0
#s = 0
def xch(self, xtype1, xtype2, xsymbol, xquantity, xprice ):
print("Formulating the order...")
#Time to sort the parameters
#xtype1...
Errors
[verl#verlpc Interface]$ python main.py
Loading Accounts...
CRYPTOANAYLISIS: PREDICTOR
> order 0 0 0 0 0
Traceback (most recent call last):
File "main.py", line 99, in <module>
m.initAccounts()
File "main.py", line 92, in initAccounts
prompt.cmdloop('CRYPTOANAYLISIS: PREDICTOR')
File "/usr/lib/python3.7/cmd.py", line 138, in cmdloop
stop = self.onecmd(line)
File "/usr/lib/python3.7/cmd.py", line 217, in onecmd
return func(arg)
File "main.py", line 50, in do_order
market.Market.xch(*args[0], *args[1], *args[2], *args[3], *args[4])
IndexError: tuple index out of range

First you have to create instance of this class
m = market.Market()
And then you have use single *args
m.xch(*args)
or many arguments but without *
m.xch(args[0], args[1], args[2], args[3], args[4])
Working example
class Market():
def xch(self, xtype1, xtype2, xsymbol, xquantity, xprice ):
print("Formulating the order...", xtype1, xtype2, xsymbol, xquantity, xprice)
args = [1,2,3,4,5]
m = Market()
m.xch(*args)
EDIT: you have to use do_order(...)
with * in definiton and when you run it
def do_order(*args):
m = Market()
m.xch(*args)
args = [1,2,3,4,5]
do_order(*args)
or without * in both places
def do_order(args):
m = Market()
m.xch(*args)
args = [1,2,3,4,5]
do_order(args)

TypeError: object array may be self-referencing python

So, I ran into this error:
temp7:
1.68219771725e-06
temp6:
[[array([-60800056.33037408], dtype=object)]]
Exception in thread Thread-4:
Traceback (most recent call last):
File "/usr/lib/python2.7/threading.py", line 810, in __bootstrap_inner
self.run()
File "/opt/ros/indigo/lib/python2.7/dist-packages/rospy/timer.py", line 223, in run
self._callback(TimerEvent(last_expected, last_real, current_expected, current_real, last_duration))
File "/home/husky/catkin_ws/src/huskybot_ekf_v2/src/meas_update.py", line 194, in meas_update_step
thisJ = temp7 * math.exp(temp6)
It seems like on the very last line thisJ =temp7* math.exp(temp6), that I can't take the exponent of temp6. Does anyone know the cause?
for i in range(0,len(line_features)-1):
for k in range(0, len(m)):
mkx = m[k][0]
mky = m[k][1]
posex = predicted_pose.x
posey = predicted_pose.y
poseth = predicted_pose.theta
distx = math.pow((mkx - posex), 2);
disty = math.pow((mky - posey), 2);
q = distx + disty
angle = math.atan2(mky-posey, mkx-posex) - poseth
expected_meas = numpy.array([[math.sqrt(q)],
[angle],
[0]
])
H = numpy.array([[-(mkx-posex)/math.sqrt(q), -(mky-posey)/math.sqrt(q) , 0],
[(mky-posey)/q, -(mkx-posex)/q, -1],
[0 , 0, 0]])
Qt = 0.5
innovation_cov = H*predicted_cov*numpy.transpose(H)+Qt
innovation = ave_meas_dist - expected_meas
dd = [[2]]
#dd = array[0]
#print 0.5*numpy.array(dd)
temp5 = -1/2 * numpy.dot(numpy.transpose(innovation), numpy.linalg.inv(innovation_cov))
temp6 = numpy.dot(temp5, innovation)
print math.sqrt(numpy.linalg.det(2*math.pi*innovation_cov)) * numpy.array(dd)
temp7 = math.sqrt(numpy.linalg.det(2*math.pi*innovation_cov))
thisJ =temp7* math.exp(temp6)

I tried to reproduce the error. The error I got is:
TypeError: must be real number, not list
If the error is same then it was solved by doing this:
thisJ = temp7 * math.exp(temp6[0][0])
Because temp6 type is list containing list containing array object. Please do correct me if I am wrong.

Runtime Exception. Exception in python callback function evaluation:

I am working on an assignment for Coursera's Machine Learning: Regression course. I am using the kc_house_data.gl/ dataset and GraphLab Create. I am adding new variables to train_data and test_data that are combinations of old variables. Then I take the mean of all these variables. These are the variables I am adding:
bedrooms_squared = bedrooms * bedrooms
bed_bath_rooms = bedrooms*bathrooms
log_sqft_living = log(sqft_living)
lat_plus_long = lat + long
Here is my code:
train_data['bedrooms_squared'] = train_data['bedrooms'].apply(lambda x: x**2)
test_data['bedrooms_squared'] = test_data['bedrooms'].apply(lambda x: x**2)
# create the remaining 3 features in both TEST and TRAIN data
train_data['bed_bath_rooms'] = train_data.apply(lambda row: row['bedrooms'] * row['bathrooms'])
test_data['bed_bath_rooms'] = test_data.apply(lambda row: row['bedrooms'] * row['bathrooms'])
train_data['log_sqft_living'] = train_data['sqft_living'].apply(lambda x: log(x))
test_data['log_sqft_living'] = test_data['bedrooms'].apply(lambda x: log(x))
train_data['lat_plus_long'] = train_data.apply(lambda row: row['lat'] + row['long'])
train_data['lat_plus_long'] = train_data.apply(lambda row: row['lat'] + row['long'])
test_data['bedrooms_squared'].mean()
test_data['bed_bath_rooms'].mean()
test_data['log_sqft_living'].mean()
test_data['lat_plus_long'].mean()
This is the error I'm getting:
RuntimeError: Runtime Exception. Exception in python callback function evaluation:
ValueError('math domain error',):
Traceback (most recent call last):
File "graphlab\cython\cy_pylambda_workers.pyx", line 426, in graphlab.cython.cy_pylambda_workers._eval_lambda
File "graphlab\cython\cy_pylambda_workers.pyx", line 169, in graphlab.cython.cy_pylambda_workers.lambda_evaluator.eval_simple
File "<ipython-input-13-1cdbcd5f5d9b>", line 5, in <lambda>
ValueError: math domain error
I have no idea what this means. Any idea on what caused it and how I fix it? Thanks.

Your problem is that log is receiving a negative number.
log is defined only for numbers greater than zero.
You need to check your values.

Please add/learn exceptions to make your code more robust:
try:
train_data['log_sqft_living'] = train_data['sqft_living'].apply(lambda x: log(x))
test_data['log_sqft_living'] = test_data['bedrooms'].apply(lambda x: log(x))
train_data['lat_plus_long'] = train_data.apply(lambda row: row['lat'] + row['long'])
train_data['lat_plus_long'] = train_data.apply(lambda row: row['lat'] + row['long'])
test_data['bedrooms_squared'].mean()
test_data['bed_bath_rooms'].mean()
test_data['log_sqft_living'].mean()
test_data['lat_plus_long'].mean()
except e as Exception:
print "ERROR in function:", e

R DTW multivariate series with asymmetric step fails to compute alignment

I'm using the DTW implementation found in R along with the python bindings in order to verify the effects of changing different parameters(like local constraint, local distance function and others) for my data. The data represents feature vectors that an audio processing frontend outputs(MFCC). Because of this I am dealing with multivariate time series, each feature vector has a size of 8. The problem I'm facing is when I try to use certain local constraints ( or step patterns ) I get the following error:
Error in if (is.na(gcm$distance)) { : argument is of length zero
Traceback (most recent call last):
File "r_dtw_simplified.py", line 32, in <module>
alignment = R.dtw(canDist, rNull, "Euclidean", stepPattern, "none", True, Fa
lse, True, False )
File "D:\Python27\lib\site-packages\rpy2\robjects\functions.py", line 86, in _
_call__
return super(SignatureTranslatedFunction, self).__call__(*args, **kwargs)
File "D:\Python27\lib\site-packages\rpy2\robjects\functions.py", line 35, in _
_call__
res = super(Function, self).__call__(*new_args, **new_kwargs)
rpy2.rinterface.RRuntimeError: Error in if (is.na(gcm$distance)) { : argument is
of length zero
Because the process of generating and adapting the input data is complicated I only made a simplified script to ilustrate the error i'm receiving.
#data works
#reference = [[-0.126678, -1.541763, 0.29985, 1.719757, 0.755798, -3.594681, -1.492798, 3.493042], [-0.110596, -1.638184, 0.128174, 1.638947, 0.721085, -3.247696, -0.920013, 3.763977], [-0.022415, -1.643539, -0.130692, 1.441742, 1.022064, -2.882172, -0.952225, 3.662842], [0.071259, -2.030411, -0.531891, 0.835114, 1.320419, -2.432281, -0.469116, 3.871094], [0.070526, -2.056702, -0.688293, 0.530396, 1.962128, -1.681915, -0.368973, 4.542419], [0.047745, -2.005127, -0.798203, 0.616028, 2.146988, -1.895874, 0.371597, 4.090881], [0.013962, -2.162796, -1.008545, 0.363495, 2.062866, -0.856613, 0.543884, 4.043335], [0.066757, -2.152969, -1.087097, 0.257263, 2.592697, -0.422424, -0.280533, 3.327576], [0.123123, -2.061035, -1.012863, 0.389282, 2.50206, 0.078186, -0.887711, 2.828247], [0.157455, -2.060425, -0.790344, 0.210419, 2.542114, 0.016983, -0.959274, 1.916504], [0.029648, -2.128204, -1.047318, 0.116547, 2.44899, 0.166534, -0.677551, 2.49231], [0.158554, -1.821365, -1.045044, 0.374207, 2.426712, 0.406952, -1.055084, 2.543762], [0.077026, -1.863235, -1.14827, 0.277069, 2.669067, 0.362549, -1.294342, 1.66748], [0.101822, -1.800293, -1.126801, 0.364594, 2.503815, 0.294846, -0.881302, 1.281616], [0.166138, -1.627762, -0.866013, 0.494476, 2.450668, 0.569, -1.392868, 0.651184], [0.225006, -1.596069, -1.07634, 0.550049, 2.167435, 0.554123, -1.432983, 1.166931], [0.114777, -1.462769, -0.793167, 0.565704, 2.183792, 0.345978, -1.410919, 0.708679], [0.144028, -1.444458, -0.831985, 0.536652, 2.222366, 0.330368, -0.715149, 0.517212], [0.147888, -1.450577, -0.809372, 0.479584, 2.271378, 0.250763, -0.540359, -0.036072], [0.090714, -1.485474, -0.888153, 0.268768, 2.001221, 0.412537, -0.698868, 0.17157], [0.11972, -1.382767, -0.890457, 0.218414, 1.666519, 0.659592, -0.069641, 0.914307], [0.189774, -1.18428, -0.785797, 0.106659, 1.429977, 0.195236, 0.627029, 0.503296], [0.194702, -1.098068, -0.956818, 0.020386, 1.369247, 0.10437, 0.641724, 0.410767], [0.215134, -1.069092, -1.11644, 0.283234, 1.313507, 0.110962, 0.600861, 0.752869], [0.216766, -1.065338, -1.047974, 0.080231, 1.500702, -0.113388, 0.712646, 0.914307], [0.259933, -0.964386, -0.981369, 0.092224, 1.480667, -0.00238, 0.896255, 0.665344], [0.265991, -0.935257, -0.93779, 0.214966, 1.235275, 0.104782, 1.33754, 0.599487], [0.266098, -0.62619, -0.905792, 0.131409, 0.402908, 0.103363, 1.352814, 1.554688], [0.273468, -0.354691, -0.709579, 0.228027, 0.315125, -0.15564, 0.942123, 1.024292], [0.246429, -0.272522, -0.609924, 0.318604, -0.007355, -0.165756, 1.07019, 1.087708], [0.248596, -0.232468, -0.524887, 0.53009, -0.476334, -0.184479, 1.088089, 0.667358], [0.074478, -0.200455, -0.058411, 0.662811, -0.111923, -0.686462, 1.205154, 1.271912], [0.063065, -0.080765, 0.065552, 0.79071, -0.569946, -0.899506, 0.875687, 0.095215], [0.117706, -0.270584, -0.021027, 0.723694, -0.200073, -0.365158, 0.892624, -0.152466], [0.00148, -0.075348, 0.017761, 0.757507, 0.719299, -0.355362, 0.749329, 0.315247], [0.035034, -0.110794, 0.038559, 0.949677, 0.478699, 0.005951, 0.097305, -0.388245], [-0.101944, -0.392487, 0.401886, 1.154938, 0.199127, 0.117371, -0.070007, -0.562439], [-0.083282, -0.388657, 0.449066, 1.505951, 0.46405, -0.566208, 0.216293, -0.528076], [-0.152054, -0.100113, 0.833054, 1.746857, 0.085861, -1.314102, 0.294632, -0.470947], [-0.166672, -0.183777, 0.988373, 1.925262, -0.202057, -0.961441, 0.15242, 0.594421], [-0.234573, -0.227707, 1.102112, 1.802002, -0.382492, -1.153336, 0.29335, 0.074036], [-0.336426, 0.042435, 1.255096, 1.804535, -0.610153, -0.810745, 1.308441, 0.599854], [-0.359344, 0.007248, 1.344543, 1.441559, -0.758286, -0.800079, 1.0233, 0.668213], [-0.321823, 0.027618, 1.1521, 1.509827, -0.708267, -0.668152, 1.05722, 0.710571], [-0.265335, 0.012344, 1.491501, 1.844971, -0.584137, -1.042419, -0.449188, 0.5354], [-0.302399, 0.049698, 1.440643, 1.674866, -0.626633, -1.158554, -0.906937, 0.405579], [-0.330276, 0.466675, 1.444153, 0.855499, -0.645447, -0.352158, 0.730423, 0.429932], [-0.354721, 0.540207, 1.570786, 0.626648, -0.897446, -0.007416, 0.174042, 0.100525], [-0.239609, 0.669983, 0.978851, 0.85321, -0.156784, 0.107986, 0.915054, 0.114197], [-0.189346, 0.930756, 0.824295, 0.516083, -0.339767, -0.206314, 0.744049, -0.36377]]
#query = [[0.387268, -1.21701, -0.432266, -1.394104, -0.458984, -1.469788, 0.12764, 2.310059], [0.418091, -1.389526, -0.150146, -0.759155, -0.578003, -2.123199, 0.276001, 3.022339], [0.264694, -1.526886, -0.238907, -0.511108, -0.90683, -2.699249, 0.692032, 2.849854], [0.246628, -1.675171, -0.533432, 0.070007, -0.392151, -1.739227, 0.534485, 2.744019], [0.099335, -1.983826, -0.985291, 0.428833, 0.26535, -1.285583, -0.234451, 2.4729], [0.055893, -2.108063, -0.401825, 0.860413, 0.724106, -1.959137, -1.360458, 2.350708], [-0.131592, -1.928314, -0.056213, 0.577698, 0.859146, -1.812286, -1.21669, 2.2052], [-0.162796, -2.149933, 0.467239, 0.524231, 0.74913, -1.829498, -0.741913, 1.616577], [-0.282745, -1.971008, 0.837616, 0.56427, 0.198288, -1.826935, -0.118027, 1.599731], [-0.497223, -1.578705, 1.277298, 0.682983, 0.055084, -2.032562, 0.64151, 1.719238], [-0.634232, -1.433258, 1.760513, 0.550415, -0.053787, -2.188568, 1.666687, 1.611938], [-0.607498, -1.302826, 1.960556, 1.331726, 0.417633, -2.271973, 2.095001, 0.9823], [-0.952957, -0.222076, 0.772064, 2.062256, -0.295258, -1.255371, 3.450974, -0.047607], [-1.210587, 1.00061, 0.036392, 1.952209, 0.470123, 0.231628, 2.670502, -0.608276], [-1.213287, 0.927002, -0.414825, 2.104065, 1.160126, 0.088898, 1.32959, -0.018311], [-1.081558, 1.007751, -0.337509, 1.7146, 0.653687, 0.297089, 1.916733, -0.772461], [-1.064804, 1.284302, -0.393585, 2.150635, 0.132294, 0.443298, 1.967575, 0.775513], [-0.972366, 1.039734, -0.588135, 1.413818, 0.423813, 0.781494, 1.977509, -0.556274], [-0.556381, 0.591309, -0.678314, 1.025635, 1.094284, 2.234711, 1.504013, -1.71875], [-0.063477, 0.626129, 0.360489, 0.149902, 0.92804, 0.936493, 1.203018, 0.264282], [0.162003, 0.577698, 0.956863, -0.477051, 1.081161, 0.817749, 0.660843, -0.428711], [-0.049515, 0.423615, 0.82489, 0.446228, 1.323853, 0.562775, -0.144196, 1.145386], [-0.146851, 0.171906, 0.304871, 0.320435, 1.378937, 0.673004, 0.188416, 0.208618], [0.33992, -2.072418, -0.447968, 0.526794, -0.175858, -1.400299, -0.452454, 1.396606], [0.226089, -2.183441, -0.301071, -0.475159, 0.834961, -2.191864, -1.092361, 2.434814], [0.279556, -2.073181, -0.517639, -0.766479, 0.974808, -2.070374, -2.003891, 2.706421], [0.237961, -1.9245, -0.708435, -0.582153, 1.285934, -1.75882, -2.146164, 2.369995], [0.149658, -1.703705, -0.539749, -0.215332, 1.369705, -1.484802, -1.506256, 1.04126], [0.078735, -1.719543, 0.157013, 0.382385, 1.100998, -0.223755, 0.021683, -0.545654], [0.106003, -1.404358, 0.372345, 1.881165, -0.292511, -0.263855, 1.579529, -1.426025], [0.047729, -1.198608, 0.600769, 1.901123, -1.106949, 0.128815, 1.293701, -1.364258], [0.110748, -0.894348, 0.712601, 1.728699, -1.250381, 0.674377, 0.812302, -1.428833], [0.085754, -0.662903, 0.794312, 1.102844, -1.234283, 1.084442, 0.986938, -1.10022], [0.140823, -0.300323, 0.673508, 0.669983, -0.551453, 1.213074, 1.449326, -1.567261], [0.03743, 0.550293, 0.400909, -0.174622, 0.355301, 1.325867, 0.875854, 0.126953], [-0.084885, 1.128906, 0.292099, -0.248779, 0.722961, 0.873871, -0.409515, 0.470581], [0.019684, 0.947754, 0.19931, -0.306274, 0.176849, 1.431702, 1.091507, 0.701416], [-0.094162, 0.895203, 0.687378, -0.229065, 0.549088, 1.376953, 0.892303, -0.642334], [-0.727692, 0.626495, 0.848877, 0.521362, 1.521912, -0.443481, 1.247238, 0.197388], [-0.82048, 0.117279, 0.975174, 1.487244, 1.085281, -0.567993, 0.776093, -0.381592], [-0.009827, -0.553009, -0.213135, 0.837341, 0.482712, -0.939423, 0.140884, 0.330566], [-0.018127, -1.362335, -0.199265, 1.260742, 0.005188, -1.445068, -1.159653, 1.220825], [0.186172, -1.727814, -0.246552, 1.544128, 0.285416, 0.081848, -1.634003, -0.47522], [0.193649, -1.144043, -0.334854, 1.220276, 1.241302, 1.554382, 0.57048, -1.334961], [0.344604, -1.647461, -0.720749, 0.993774, 0.585709, 0.953522, -0.493042, -1.845703], [0.37471, -1.989471, -0.518555, 0.555908, -0.025787, 0.148132, -1.463425, -0.844849], [0.34523, -1.821625, -0.809418, 0.59137, -0.577927, 0.037903, -2.067764, -0.519531], [0.413193, -1.503876, -0.752243, 0.280396, -0.236206, 0.429932, -1.684097, -0.724731], [0.331299, -1.349243, -0.890121, -0.178589, -0.285721, 0.809875, -2.012329, -0.157227], [0.278946, -1.090057, -0.670441, -0.477539, -0.267105, 0.446045, -1.95668, 0.501343], [0.127304, -0.977112, -0.660324, -1.011658, -0.547409, 0.349182, -1.357574, 1.045654], [0.217728, -0.793182, -0.496262, -1.259949, -0.128937, 0.38855, -1.513306, 1.863647], [0.240143, -0.891541, -0.619995, -1.478577, -0.361481, 0.258362, -1.630585, 1.841064], [0.241547, -0.758453, -0.515442, -1.370605, -0.428238, 0.23996, -1.469406, 1.307617], [0.289948, -0.714661, -0.533798, -1.574036, 0.017929, -0.368317, -1.290283, 0.851563], [0.304916, -0.783752, -0.459915, -1.523621, -0.107651, -0.027649, -1.089905, 0.969238], [0.27179, -0.795593, -0.352432, -1.597656, -0.001678, -0.06189, -1.072495, 0.637329], [0.301956, -0.823578, -0.152115, -1.637634, 0.2034, -0.214508, -1.315491, 0.773071], [0.282486, -0.853271, -0.162094, -1.561096, 0.15686, -0.289307, -1.076874, 0.673706], [0.299881, -0.97052, -0.051086, -1.431152, -0.074692, -0.32428, -1.385452, 0.684326], [0.220886, -1.072266, -0.269531, -1.038269, 0.140533, -0.711273, -1.7453, 1.090332], [0.177628, -1.229126, -0.274292, -0.943481, 0.483246, -1.214447, -2.026321, 0.719971], [0.176987, -1.137543, -0.007645, -0.794861, 0.965118, -1.084717, -2.37677, 0.598267], [0.135727, -1.36795, 0.09462, -0.776367, 0.946655, -1.157959, -2.794403, 0.226074], [0.067337, -1.648987, 0.535721, -0.665833, 1.506119, -1.348755, -3.092728, 0.281616], [-0.038101, -1.437347, 0.983917, -0.280762, 1.880722, -1.351318, -3.002258, -0.599609], [-0.152573, -1.146027, 0.717545, -0.60321, 2.126541, -0.59198, -2.282028, -1.048584], [-0.113525, -0.629669, 0.925323, 0.465393, 2.368698, -0.352661, -1.969391, -0.915161], [-0.140121, -0.311951, 0.884262, 0.809021, 1.557693, -0.552429, -1.776062, -0.925537], [-0.189423, -0.117767, 0.975174, 1.595032, 1.284485, -0.698639, -2.007202, -1.307251], [-0.048874, -0.176941, 0.820679, 1.306519, 0.584259, -0.913147, -0.658066, -0.630981], [-0.127594, 0.33313, 0.791336, 1.400696, 0.685577, -1.500275, -0.657959, -0.207642], [-0.044128, 0.653351, 0.615326, 0.476685, 1.099625, -0.902893, -0.154449, 0.325073], [-0.150223, 1.059845, 1.208405, -0.038635, 0.758667, 0.458038, -0.178909, -0.998657], [-0.099854, 1.127197, 0.789871, -0.013611, 0.452805, 0.736176, 0.948273, -0.236328], [-0.250275, 1.188568, 0.935989, 0.34314, 0.130463, 0.879913, 1.669037, 0.12793], [-0.122818, 1.441223, 0.670029, 0.389526, -0.15274, 1.293549, 1.22908, -1.132568]]
#this one doesn't
reference = [[-0.453598, -2.439209, 0.973587, 1.362091, -0.073654, -1.755112, 1.090057, 4.246765], [-0.448502, -2.621201, 0.723282, 1.257324, 0.26619, -1.375351, 1.328735, 4.46991], [-0.481247, -2.29718, 0.612854, 1.078033, 0.309708, -2.037506, 1.056305, 3.181702], [-0.42482, -2.306702, 0.436157, 1.529907, 0.50708, -1.930069, 0.653198, 3.561768], [-0.39032, -2.361343, 0.589294, 1.965607, 0.611801, -2.417084, 0.035675, 3.381104], [-0.233444, -2.281525, 0.703171, 2.17868, 0.519257, -2.474442, -0.502808, 3.569153], [-0.174652, -1.924591, 0.180267, 2.127075, 0.250626, -2.208527, -0.396591, 2.565552], [-0.121078, -1.53801, 0.234344, 2.221039, 0.845367, -1.516205, -0.174149, 1.298645], [-0.18631, -1.047806, 0.629654, 2.073303, 0.775024, -1.931076, 0.382706, 2.278442], [-0.160477, -0.78743, 0.694214, 1.917572, 0.834885, -1.574707, 0.780045, 2.370422], [-0.203659, -0.427246, 0.726486, 1.548767, 0.465698, -1.185379, 0.555206, 2.619629], [-0.208298, -0.393707, 0.771881, 1.646484, 0.612946, -0.996277, 0.658539, 2.499146], [-0.180679, -0.166656, 0.689209, 1.205994, 0.3918, -1.051483, 0.771072, 1.854553], [-0.1978, 0.082764, 0.723541, 1.019104, 0.165405, -0.127533, 1.0522, 0.552368], [-0.171127, 0.168533, 0.529541, 0.584839, 0.702011, -0.36525, 0.711792, 1.029114], [-0.224243, 0.38765, 0.916031, 0.45108, 0.708923, -0.059326, 1.016312, 0.437561], [-0.217072, -0.981766, 1.67363, 1.864014, 0.050812, -2.572815, -0.22937, 0.757996], [-0.284714, -0.784927, 1.720383, 1.782379, -0.093414, -2.492111, 0.623398, 0.629028], [-0.261169, -0.427979, 1.680038, 1.585358, 0.067093, -1.8181, 1.276291, 0.838989], [-0.183075, -0.08197, 1.094147, 1.120392, -0.117752, -0.86142, 1.94194, 0.966858], [-0.188919, 0.121521, 1.277664, 0.90979, 0.114288, -0.880875, 1.920517, 0.95752], [-0.226868, 0.338455, 0.78067, 0.803009, 0.347092, -0.387955, 0.641296, 0.374634], [-0.206329, 0.768158, 0.759537, 0.264099, 0.15979, 0.152618, 0.911636, -0.011597], [-0.230453, 0.495941, 0.547165, 0.137604, 0.36377, 0.594406, 1.168839, 0.125916], [0.340851, -0.382736, -1.060455, -0.267792, 1.1306, 0.595047, -1.544922, -1.6828], [0.341492, -0.325836, -1.07164, -0.215607, 0.895645, 0.400177, -0.773956, -1.827515], [0.392075, -0.305389, -0.885422, -0.293427, 0.993225, 0.66655, -1.061218, -1.730713], [0.30191, -0.339005, -0.877853, 0.153992, 0.986588, 0.711823, -1.100525, -1.648376], [0.303574, -0.491241, -1.000183, 0.075378, 0.686295, 0.752792, -1.192123, -1.744568], [0.315781, -0.629456, -0.996063, 0.224731, 1.074173, 0.757736, -1.170807, -2.08313], [0.313675, -0.804688, -1.00325, 0.431641, 0.685883, 0.538879, -0.988373, -2.421326], [0.267181, -0.790329, -0.726974, 0.853027, 1.369629, -0.213638, -1.708023, -1.977844], [0.304459, -0.935257, -0.778061, 1.042633, 1.391861, -0.296768, -1.562164, -2.014099], [0.169754, -0.792953, -0.481842, 1.404236, 0.766983, -0.29805, -1.587265, -1.25531], [0.15918, -0.9814, -0.197662, 1.748718, 0.888367, -0.880234, -1.64949, -1.359802], [0.028244, -0.772934, -0.186172, 1.594238, 0.863571, -1.224701, -1.153183, -0.292664], [-0.020401, -0.461578, 0.368088, 1.000366, 1.079636, -0.389603, -0.144409, 0.651733], [0.018555, -0.725418, 0.632599, 1.707336, 0.535049, -1.783859, -0.916122, 1.557007], [-0.038971, -0.797668, 0.820419, 1.483093, 0.350494, -1.465073, -0.786453, 1.370361], [-0.244888, -0.469513, 1.067978, 1.028809, 0.4879, -1.796585, -0.77887, 1.888977], [-0.260193, -0.226593, 1.141754, 1.21228, 0.214005, -1.200943, -0.441177, 0.532715], [-0.165283, 0.016129, 1.263016, 0.745514, -0.211288, -0.802368, 0.215698, 0.316406], [-0.353134, 0.053787, 1.544189, 0.21106, -0.469086, -0.485367, 0.767761, 0.849548], [-0.330215, 0.162704, 1.570053, 0.304718, -0.561172, -0.410294, 0.895126, 0.858093], [-0.333847, 0.173904, 1.56958, 0.075531, -0.5569, -0.259552, 1.276764, 0.749084], [-0.347107, 0.206665, 1.389832, 0.50473, -0.721664, -0.56955, 1.542618, 0.817444], [-0.299057, 0.140244, 1.402924, 0.215363, -0.62767, -0.550461, 1.60788, 0.506958], [-0.292084, 0.052063, 1.463348, 0.290497, -0.462875, -0.497452, 1.280609, 0.261841], [-0.279877, 0.183548, 1.308609, 0.305756, -0.6483, -0.374771, 1.647781, 0.161865], [-0.28389, 0.27916, 1.148636, 0.466736, -0.724442, -0.21991, 1.819901, -0.218872], [-0.275528, 0.309753, 1.192856, 0.398163, -0.828781, -0.268066, 1.763672, 0.116089], [-0.275284, 0.160019, 1.200623, 0.718628, -0.925552, -0.026596, 1.367447, 0.174866], [-0.302795, 0.383438, 1.10556, 0.441833, -0.968323, -0.137375, 1.851791, 0.357971], [-0.317078, 0.22876, 1.272217, 0.462219, -0.855789, -0.294296, 1.593994, 0.127502], [-0.304932, 0.207718, 1.156189, 0.481506, -0.866776, -0.340027, 1.670105, 0.657837], [-0.257217, 0.155655, 1.041428, 0.717926, -0.761597, -0.17244, 1.114151, 0.653503], [-0.321426, 0.292358, 0.73848, 0.422607, -0.850754, -0.057907, 1.462357, 0.697754], [-0.34642, 0.361526, 0.69722, 0.585175, -0.464508, -0.26651, 1.860596, 0.106201], [-0.339844, 0.584229, 0.542603, 0.184937, -0.341263, 0.085648, 1.837311, 0.160461], [-0.32338, 0.661224, 0.512833, 0.319702, -0.195572, 0.004028, 1.046799, 0.233704], [-0.346329, 0.572388, 0.385986, 0.118988, 0.057556, 0.039001, 1.255081, -0.18573], [-0.383392, 0.558395, 0.553391, -0.358612, 0.443573, -0.086014, 0.652878, 0.829956], [-0.420395, 0.668991, 0.64856, -0.021271, 0.511475, 0.639221, 0.860474, 0.463196], [-0.359039, 0.748672, 0.522964, -0.308899, 0.717194, 0.218811, 0.681396, 0.606812], [-0.323914, 0.942627, 0.249069, -0.418365, 0.673599, 0.797974, 0.162674, 0.120361], [-0.411301, 0.92775, 0.493332, -0.286346, 0.165054, 0.63446, 1.085571, 0.120789], [-0.346191, 0.632309, 0.635056, -0.402496, 0.143814, 0.785614, 0.952164, 0.482727], [-0.203812, 0.789261, 0.240433, -0.47699, -0.12912, 0.91832, 1.145493, 0.052002], [-0.048203, 0.632095, 0.009583, -0.53833, 0.232727, 1.293045, 0.308151, 0.188904], [-0.062393, 0.732315, 0.06694, -0.697144, 0.126221, 0.864578, 0.581635, -0.088379]]
query = [[-0.113144, -3.316223, -1.101563, -2.128418, 1.853867, 3.61972, 1.218185, 1.71228], [-0.128952, -3.37915, -1.152237, -2.033081, 1.860199, 4.008179, 0.445938, 1.665894], [-0.0392, -2.976654, -0.888245, -1.613953, 1.638641, 3.849518, 0.034073, 0.768188], [-0.146042, -2.980713, -1.044113, -1.44397, 0.954514, 3.20929, -0.232422, 1.050781], [-0.155029, -2.997192, -1.064438, -1.369873, 0.67688, 2.570709, -0.855347, 1.523438], [-0.102341, -2.686401, -1.029648, -1.00531, 0.950089, 1.933228, -0.526367, 1.598633], [-0.060272, -2.538727, -1.278259, -0.65332, 0.630875, 1.459717, -0.264038, 1.872925], [0.064087, -2.592682, -1.112823, -0.775024, 0.848618, 0.810883, 0.298965, 2.312134], [0.111557, -2.815277, -1.203506, -1.173584, 0.54863, 0.46756, -0.023071, 3.029053], [0.266068, -2.624786, -1.089066, -0.864136, 0.055389, 0.619446, -0.160965, 2.928589], [0.181488, -2.31073, -1.307785, -0.720276, 0.001297, 0.534668, 0.495499, 2.989502], [0.216202, -2.25354, -1.288193, -0.902039, -0.152283, -0.060791, 0.566315, 2.911621], [0.430084, -2.0289, -1.099594, -1.091736, -0.302505, -0.087799, 0.955963, 2.677002], [0.484253, -1.412842, -0.881882, -1.087158, -1.064072, -0.145935, 1.437683, 2.606567], [0.339081, -1.277222, -1.24498, -1.048279, -0.219498, 0.448517, 1.168625, 0.563843], [0.105728, 0.138275, -1.01413, -0.489868, 1.319275, 1.604645, 1.634003, -0.94812], [-0.209061, 1.025665, 0.180405, 0.955566, 1.527405, 0.91745, 1.951233, -0.40686], [-0.136993, 1.332275, 0.639862, 1.277832, 1.277313, 0.361267, 0.390717, -0.728394], [-0.217758, 1.416718, 1.080002, 0.816101, 0.343933, -0.154175, 1.10347, -0.568848]]
reference = np.array( reference )
query = np.array( query )
rpy2.robjects.numpy2ri.activate()
# Set up our R namespaces
R = rpy2.robjects.r
rNull = R("NULL")
rprint = rpy2.robjects.globalenv.get("print")
rplot = rpy2.robjects.r('plot')
distConstr = rpy2.robjects.r('proxy::dist')
DTW = importr('dtw')
stepName = "asymmetricP05"
stepPattern = rpy2.robjects.r( stepName )
canDist = distConstr( reference, query, "Euclidean" ) #
alignment = R.dtw(canDist, rNull, "Euclidean", stepPattern, "none", True, False, True, False )
For some series the script doesn't generate the error but there are some which do. See the commented lines for examples. It is worth noting that for the classic constraint this error does not appear. I am thinking that perhaps I have not set-up something correct but I am no expert in python nor in R so that is why I was hoping that others who have used the R DTW can help me on this. I am sorry for the long lines for reference and query (the data is from outputting the MFCC's of a 2 second wav file).

One of the two series is too short to be compatible with the fancy step pattern you chose. Use the common symmetric2 pattern, which does not restrict slopes, before the more exotic ones.

Categories

Resources