# converting from "double" to "float" requires a narrowing conversion

I collect the project at compilation produces errors, about the requirement of the employee of conversion. Help me fix this error. I collect under Windows visual studio 2017

here is the source code enter link description here

boost and Armadillo lib set.

Error C2398 Element "2": converting from "double" to "float" requires a narrowing conversion

```
error C2398: Element "4": converting from "initializer list" to "std :: pair
<const _Kty, _Ty>" requires a narrowing conversion
with
1> [
1> _Kty=float,
1> _Ty=std::map<int,std::vector<float,std::allocator<float>>,std::less<int>,std::allocator<std::pair<const int,std::vector<float,std::allocator<float>>>>>
//=================================================================================================
// Copyright (C) 2016 Olivier Mallet - All Rights Reserved
//=================================================================================================
#ifndef COEFF_ADF_HPP
#define COEFF_ADF_HPP
// critical values response surface coefficients
// for each probability or significance level the associated quantile is estimated via the following regression:
// C(k,T)(p) = B0 + B1/T + B2/T^2 + B3*(k/T) + B4*(k/T)^2 + e(k,T)
// with T = N - k - 1 where N is the sample size, k is the number of lags, T is the effective number of observations and e(k,T) the residuals
// B0 is the asymptotic critical value of the test for the p significance level
// this is an example, in some cases we will have more term in 1/T^i or (k/T)^i
// in the example above, index 0 would contain B0, B1 and B2 and index 1 B3 and B4
// The choice of the number of regressors has been made looking at the precision of each model estimation by comparing with Monte-Carlo simulated critical values, and taking into account the regression quality that is the parameters heteroskedasticity consistent standard errors and t values, the regression standard error, the Akaike information criterion, the goodness of fit R and the residuals auto-correlation.
// We have taken care especially of the quality of the 1%, 5% and 10% confidence levels regressions.
// We have used the methodology explained in "Lag Order and Critical Values of the Augmented Dickey-Fuller Test" by Cheung and Lai (1995), we have extended the sample sizes and number of lags and have used more replications to get more accurate results.
static const std::map<std::string, std::map<float, std::map<int, std::vector<float>>>> coeff_adf=
{
{"nc",{
{0.001f,{
{0, {-3.281702f, -4.768726f, -272.9072f, 5579.295f, -45226.96f}},
{1, {0.5493214f, -2.698764f}}}},
{0.005f,{
{0, {-2.794503f, -3.1995f, -146.9075f, 3099.156f, -24497.04f}},
{1, {0.5505589f, -2.085242f}}}},
{0.01f,{
{0, {-2.561287f, -2.142564f, -121.1985f, 2584.976f, -20410.33f}},
{1, {0.4760427f, -1.539142f}}}},
{0.025f,{
{0, {-2.223917f, -0.6914316f, -111.5947f, 2411.508f, -17908.34f}},
{1, {0.4181673f, -1.186685f}}}},
{0.05f,{
{0, {-1.936403f, -1.176172f, -4.937049f}},
{1, {0.4133739f, -1.106667f}}}},
{0.10f,{
{0, {-1.61316f, -0.3324831f, -1.810886f}},
{1, {0.346874f, -0.7603873f}}}},
{0.20f,{
{0, {-1.232539f, 0.6746855f, -2.034739f}},
{1, {0.2687615f, -0.4501587f}}}},
{0.50f,{
{0, {-0.5019654f, 4.022296f, -26.36976f}},
{1, {0.2869943f, 0.03450908f}}}},
{0.8f,{
{0, {0.4032327f, 4.540675f, -33.07985f}},
{1, {0.5776706f, 0.2456931f}}}},
{0.9f,{
{0, {0.8867146f, 4.419886f, -29.49461f}},
{1, {0.741675f, -0.1049857f}}}},
{0.95f,{
{0, {1.281964f, 4.550459f, -25.75607f}},
{1, {0.8509155f, -0.4257711f}}}},
{0.975f,{
{0, {1.621407f, 4.919284f, -21.08875f}},
{1, {0.9096531f, -0.6824688f}}}},
{0.99f,{
{0, {2.013246f, 5.729489f, -14.25646f}},
{1, {0.9222194f, -0.6803509f}}}},
{0.995f,{
{0, {2.278707f, 6.459439f, -6.844265f}},
{1, {0.9229393f, -0.58905f}}}},
{0.999f,{
{0, {2.82238f, 8.798691f, 15.4077f}},
{1, {0.8097516f, 0.076346f}}}}
}},
{"c",{
{0.001,{
{0, {-4.093629, -10.95619, -201.1707, 4059.445, -47421.5}},
{1, {1.135068, -5.529418}}}},
{0.005,{
{0, {-3.644943, -7.32321, -133.5845, 3116.307, -34229.66}},
{1, {0.9485738, -3.219351}}}},
{0.01,{
{0, {-3.431418, -5.638492, -117.5981, 2807.583, -28966.07}},
{1, {0.8777557, -2.478217}}}},
{0.025,{
{0, {-3.122512, -4.084893, -56.41564, 1374.696, -15404.28}},
{1, {0.7851985, -1.540707}}}},
{0.05,{
{0, {-2.861234, -3.352301, 14.44003, -393.0152}},
{1, {0.7401921, -1.162586}}}},
{0.1,{
{0, {-2.56724, -1.456952, -11.77928}},
{1, {0.6391723, -0.5805052}}}},
{0.2,{
{0, {-2.21775, -0.4380315, -5.553964}},
{1, {0.5146035, 0.07913832}}}},
{0.5,{
{0, {-1.565026, 0.5520032, 0.3320911}},
{1, {0.2884884, 1.558016}}}},
{0.8,{
{0, {-0.8648469, 1.248728, 0.2664044}},
{1, {1.179858, 1.250165}}}},
{0.9,{
{0, {-0.4406348, 1.649804, -3.58948}},
{1, {1.939274, 0.2523149}}}},
{0.95,{
{0, {-0.07812974, 1.893762, -6.304656}},
{1, {2.639824, -0.7719409}}}},
{0.975,{
{0, {0.239394, 2.14986, -8.572655}},
{1, {3.330538, -1.912599}}}},
{0.99,{
{0, {0.6091726, 2.496982, -7.439446}},
{1, {4.225463, -3.616533}}}},
{0.995,{
{0, {0.8623198, 2.874015, -5.580965}},
{1, {4.839039, -4.653141}}}},
{0.999,{
{0, {1.38689, 3.415222, 13.42629}},
{1, {6.166719, -6.678131}}}}
}},
{"ct",{
{0.001,{
{0, {-4.602904, -12.17787, -527.9003, 13769.63, -149247.9}},
{1, {1.738192, -8.439729}}}},
{0.005,{
{0, {-4.168873, -9.095683, -266.1883, 6786.044, -75171.46}},
{1, {1.449617, -4.963447}}}},
{0.01,{
{0, {-3.961001, -7.941104, -164.6046, 4154.17, -48022.67}},
{1, {1.33377, -3.942321}}}},
{0.025,{
{0, {-3.662298, -5.911063, -88.6424, 2221.722, -26744.54}},
{1, {1.16549, -2.516979}}}},
{0.05,{
{0, {-3.410894, -4.149194, -64.03544, 1624.534, -18656.29}},
{1, {1.022964, -1.462953}}}},
{0.1,{
{0, {-3.127902, -2.344014, -22.26126}},
{1, {0.8885139, -0.7515388}}}},
{0.2,{
{0, {-2.7924, -0.9703418, -9.753819}},
{1, {0.6833622, 0.4110066}}}},
{0.5,{
{0, {-2.17854, 0.6154379, 2.143893}},
{1, {0.323513, 2.654173}}}},
{0.8,{
{0, {-1.584759, 1.35046, 9.264756}},
{1, {0.8602757, 4.07367}}}},
{0.9,{
{0, {-1.250804, 2.20269, 4.417211}},
{1, {1.987795, 2.371877}}}},
{0.95,{
{0, {-0.9430168, 2.91489, -3.161068}},
{1, {2.961466, 0.795468}}}},
{0.975,{
{0, {-0.6617862, 3.388312, -7.564776}},
{1, {3.852376, -0.7378108}}}},
{0.99,{
{0, {-0.3247998, 3.833996, -9.750533}},
{1, {4.934732, -2.514723}}}},
{0.995,{
{0, {-0.09078475, 3.95775, -6.916539}},
{1, {5.73554, -3.762424}}}},
{0.999,{
{0, {0.3995264, 4.650644, -2.198471}},
{1, {7.366291, -5.53531}}}}
}},
{"ctt",{
{0.001,{
{0, {-5.013231, -12.32148, -931.3062, 26178.47, -281197.4}},
{1, {2.373687, -13.23884}}}},
{0.005,{
{0, {-4.579845, -10.82918, -406.3291, 10976.8, -123523.2}},
{1, {1.821085, -7.467968}}}},
{0.01,{
{0, {-4.376393, -9.20233, -288.6418, 7529.634, -84447.02}},
{1, {1.637286, -5.610608}}}},
{0.025,{
{0, {-4.082349, -7.139103, -167.3334, 4387.772, -50031.24}},
{1, {1.43226, -3.575693}}}},
{0.05,{
{0, {-3.833339, -5.531, -87.00663, 2242.58, -27241.12}},
{1, {1.226892, -2.0091}}}},
{0.1,{
{0, {-3.551958, -4.452909, 26.14951, -675.7979}},
{1, {1.021034, -0.6615268}}}},
{0.2,{
{0, {-3.223184, -1.522704, -13.55332}},
{1, {0.77266, 0.8243226}}}},
{0.5,{
{0, {-2.617229, 0.5046602, 7.156626}},
{1, {0.3347223, 3.860026}}}},
{0.8,{
{0, {-2.043999, 1.426968, 20.87687}},
{1, {0.7072516, 6.213741}}}},
{0.9,{
{0, {-1.743752, 2.260416, 20.26852}},
{1, {1.886585, 4.803952}}}},
{0.95,{
{0, {-1.474189, 3.330163, 11.66451}},
{1, {3.088977, 2.70093}}}},
{0.975,{
{0, {-1.219848, 4.087149, 4.610631}},
{1, {4.181104, 0.7281762}}}},
{0.99,{
{0, {-0.908479, 4.874732, -3.408405}},
{1, {5.469988, -1.455318}}}},
{0.995,{
{0, {-0.6891048, 5.363189, -7.792514}},
{1, {6.365469, -2.765692}}}},
{0.999,{
{0, {-0.2228572, 5.742693, -1.09101}},
{1, {8.303857, -5.030229}}}}
}}
};
#endif
```