In [1]:
import findspark
In [2]:
findspark.init('/home/spark/spark-2.1.0-bin-hadoop2.7')
In [3]:
import pyspark
In [4]:
from pyspark.sql import SparkSession
In [5]:
spark = SparkSession.builder.appName('NuralNet').getOrCreate()
In [6]:
df = spark.read.json('iris.json')
In [7]:
df.show()
+---------------+-----------+----------+-----------+----------+-------+
|_corrupt_record|petalLength|petalWidth|sepalLength|sepalWidth|species|
+---------------+-----------+----------+-----------+----------+-------+
|              [|       null|      null|       null|      null|   null|
|           null|        1.4|       0.2|        5.1|       3.5| setosa|
|           null|        1.4|       0.2|        4.9|       3.0| setosa|
|           null|        1.3|       0.2|        4.7|       3.2| setosa|
|           null|        1.5|       0.2|        4.6|       3.1| setosa|
|           null|        1.4|       0.2|        5.0|       3.6| setosa|
|           null|        1.7|       0.4|        5.4|       3.9| setosa|
|           null|        1.4|       0.3|        4.6|       3.4| setosa|
|           null|        1.5|       0.2|        5.0|       3.4| setosa|
|           null|        1.4|       0.2|        4.4|       2.9| setosa|
|           null|        1.5|       0.1|        4.9|       3.1| setosa|
|           null|        1.5|       0.2|        5.4|       3.7| setosa|
|           null|        1.6|       0.2|        4.8|       3.4| setosa|
|           null|        1.4|       0.1|        4.8|       3.0| setosa|
|           null|        1.1|       0.1|        4.3|       3.0| setosa|
|           null|        1.2|       0.2|        5.8|       4.0| setosa|
|           null|        1.5|       0.4|        5.7|       4.4| setosa|
|           null|        1.3|       0.4|        5.4|       3.9| setosa|
|           null|        1.4|       0.3|        5.1|       3.5| setosa|
|           null|        1.7|       0.3|        5.7|       3.8| setosa|
+---------------+-----------+----------+-----------+----------+-------+
only showing top 20 rows

In [8]:
df.printSchema()
root
 |-- _corrupt_record: string (nullable = true)
 |-- petalLength: double (nullable = true)
 |-- petalWidth: double (nullable = true)
 |-- sepalLength: double (nullable = true)
 |-- sepalWidth: double (nullable = true)
 |-- species: string (nullable = true)

In [9]:
from pyspark.sql.types import (StructField,StringType,
                               IntegerType,StructType,FloatType)
In [10]:
df.show()
+---------------+-----------+----------+-----------+----------+-------+
|_corrupt_record|petalLength|petalWidth|sepalLength|sepalWidth|species|
+---------------+-----------+----------+-----------+----------+-------+
|              [|       null|      null|       null|      null|   null|
|           null|        1.4|       0.2|        5.1|       3.5| setosa|
|           null|        1.4|       0.2|        4.9|       3.0| setosa|
|           null|        1.3|       0.2|        4.7|       3.2| setosa|
|           null|        1.5|       0.2|        4.6|       3.1| setosa|
|           null|        1.4|       0.2|        5.0|       3.6| setosa|
|           null|        1.7|       0.4|        5.4|       3.9| setosa|
|           null|        1.4|       0.3|        4.6|       3.4| setosa|
|           null|        1.5|       0.2|        5.0|       3.4| setosa|
|           null|        1.4|       0.2|        4.4|       2.9| setosa|
|           null|        1.5|       0.1|        4.9|       3.1| setosa|
|           null|        1.5|       0.2|        5.4|       3.7| setosa|
|           null|        1.6|       0.2|        4.8|       3.4| setosa|
|           null|        1.4|       0.1|        4.8|       3.0| setosa|
|           null|        1.1|       0.1|        4.3|       3.0| setosa|
|           null|        1.2|       0.2|        5.8|       4.0| setosa|
|           null|        1.5|       0.4|        5.7|       4.4| setosa|
|           null|        1.3|       0.4|        5.4|       3.9| setosa|
|           null|        1.4|       0.3|        5.1|       3.5| setosa|
|           null|        1.7|       0.3|        5.7|       3.8| setosa|
+---------------+-----------+----------+-----------+----------+-------+
only showing top 20 rows

In [11]:
data_schema = [StructField('petalLength',FloatType(),True),
              StructField('petalWidth',FloatType(),True),StructField('sepalLength',FloatType(),True),
              StructField('sepalWidth',FloatType(),True),StructField('species',StringType(),True)]
In [12]:
Struck_Final = StructType(fields=data_schema)
In [13]:
Refined_Df = spark.read.json('iris.json',schema = Struck_Final)
In [14]:
Refined_Df.show()
+-----------+----------+-----------+----------+-------+
|petalLength|petalWidth|sepalLength|sepalWidth|species|
+-----------+----------+-----------+----------+-------+
|       null|      null|       null|      null|   null|
|        1.4|       0.2|        5.1|       3.5| setosa|
|        1.4|       0.2|        4.9|       3.0| setosa|
|        1.3|       0.2|        4.7|       3.2| setosa|
|        1.5|       0.2|        4.6|       3.1| setosa|
|        1.4|       0.2|        5.0|       3.6| setosa|
|        1.7|       0.4|        5.4|       3.9| setosa|
|        1.4|       0.3|        4.6|       3.4| setosa|
|        1.5|       0.2|        5.0|       3.4| setosa|
|        1.4|       0.2|        4.4|       2.9| setosa|
|        1.5|       0.1|        4.9|       3.1| setosa|
|        1.5|       0.2|        5.4|       3.7| setosa|
|        1.6|       0.2|        4.8|       3.4| setosa|
|        1.4|       0.1|        4.8|       3.0| setosa|
|        1.1|       0.1|        4.3|       3.0| setosa|
|        1.2|       0.2|        5.8|       4.0| setosa|
|        1.5|       0.4|        5.7|       4.4| setosa|
|        1.3|       0.4|        5.4|       3.9| setosa|
|        1.4|       0.3|        5.1|       3.5| setosa|
|        1.7|       0.3|        5.7|       3.8| setosa|
+-----------+----------+-----------+----------+-------+
only showing top 20 rows

In [15]:
Na_refine=Refined_Df.na.drop()
In [16]:
Na_refine.show()
+-----------+----------+-----------+----------+-------+
|petalLength|petalWidth|sepalLength|sepalWidth|species|
+-----------+----------+-----------+----------+-------+
|        1.4|       0.2|        5.1|       3.5| setosa|
|        1.4|       0.2|        4.9|       3.0| setosa|
|        1.3|       0.2|        4.7|       3.2| setosa|
|        1.5|       0.2|        4.6|       3.1| setosa|
|        1.4|       0.2|        5.0|       3.6| setosa|
|        1.7|       0.4|        5.4|       3.9| setosa|
|        1.4|       0.3|        4.6|       3.4| setosa|
|        1.5|       0.2|        5.0|       3.4| setosa|
|        1.4|       0.2|        4.4|       2.9| setosa|
|        1.5|       0.1|        4.9|       3.1| setosa|
|        1.5|       0.2|        5.4|       3.7| setosa|
|        1.6|       0.2|        4.8|       3.4| setosa|
|        1.4|       0.1|        4.8|       3.0| setosa|
|        1.1|       0.1|        4.3|       3.0| setosa|
|        1.2|       0.2|        5.8|       4.0| setosa|
|        1.5|       0.4|        5.7|       4.4| setosa|
|        1.3|       0.4|        5.4|       3.9| setosa|
|        1.4|       0.3|        5.1|       3.5| setosa|
|        1.7|       0.3|        5.7|       3.8| setosa|
|        1.5|       0.3|        5.1|       3.8| setosa|
+-----------+----------+-----------+----------+-------+
only showing top 20 rows

In [17]:
from pyspark.ml.feature import StringIndexer
In [18]:
StringIndexer = StringIndexer(inputCol = "species", outputCol = "Species_category")
In [19]:
Na_refine.show()
StringIndexed = StringIndexer.fit(Na_refine).transform(Na_refine)
+-----------+----------+-----------+----------+-------+
|petalLength|petalWidth|sepalLength|sepalWidth|species|
+-----------+----------+-----------+----------+-------+
|        1.4|       0.2|        5.1|       3.5| setosa|
|        1.4|       0.2|        4.9|       3.0| setosa|
|        1.3|       0.2|        4.7|       3.2| setosa|
|        1.5|       0.2|        4.6|       3.1| setosa|
|        1.4|       0.2|        5.0|       3.6| setosa|
|        1.7|       0.4|        5.4|       3.9| setosa|
|        1.4|       0.3|        4.6|       3.4| setosa|
|        1.5|       0.2|        5.0|       3.4| setosa|
|        1.4|       0.2|        4.4|       2.9| setosa|
|        1.5|       0.1|        4.9|       3.1| setosa|
|        1.5|       0.2|        5.4|       3.7| setosa|
|        1.6|       0.2|        4.8|       3.4| setosa|
|        1.4|       0.1|        4.8|       3.0| setosa|
|        1.1|       0.1|        4.3|       3.0| setosa|
|        1.2|       0.2|        5.8|       4.0| setosa|
|        1.5|       0.4|        5.7|       4.4| setosa|
|        1.3|       0.4|        5.4|       3.9| setosa|
|        1.4|       0.3|        5.1|       3.5| setosa|
|        1.7|       0.3|        5.7|       3.8| setosa|
|        1.5|       0.3|        5.1|       3.8| setosa|
+-----------+----------+-----------+----------+-------+
only showing top 20 rows

In [52]:
StringIndexed.show(10)
+-----------+----------+-----------+----------+-------+----------------+
|petalLength|petalWidth|sepalLength|sepalWidth|species|Species_category|
+-----------+----------+-----------+----------+-------+----------------+
|        1.4|       0.2|        5.1|       3.5| setosa|             2.0|
|        1.4|       0.2|        4.9|       3.0| setosa|             2.0|
|        1.3|       0.2|        4.7|       3.2| setosa|             2.0|
|        1.5|       0.2|        4.6|       3.1| setosa|             2.0|
|        1.4|       0.2|        5.0|       3.6| setosa|             2.0|
|        1.7|       0.4|        5.4|       3.9| setosa|             2.0|
|        1.4|       0.3|        4.6|       3.4| setosa|             2.0|
|        1.5|       0.2|        5.0|       3.4| setosa|             2.0|
|        1.4|       0.2|        4.4|       2.9| setosa|             2.0|
|        1.5|       0.1|        4.9|       3.1| setosa|             2.0|
+-----------+----------+-----------+----------+-------+----------------+
only showing top 10 rows

In [53]:
#Df_Pandas =StringIndexed.toPandas()
In [54]:
#import pandas as  pd
#import numpy as np
In [165]:
Variables  = np.array(StringIndexed.select('petalLength','petalWidth','sepalLength','sepalWidth').collect())
In [166]:
Labels  = np.array(StringIndexed.select('Species_category').collect())
In [167]:
Variables[0:10]
Out[167]:
array([[1.39999998, 0.2       , 5.0999999 , 3.5       ],
       [1.39999998, 0.2       , 4.9000001 , 3.        ],
       [1.29999995, 0.2       , 4.69999981, 3.20000005],
       [1.5       , 0.2       , 4.5999999 , 3.0999999 ],
       [1.39999998, 0.2       , 5.        , 3.5999999 ],
       [1.70000005, 0.40000001, 5.4000001 , 3.9000001 ],
       [1.39999998, 0.30000001, 4.5999999 , 3.4000001 ],
       [1.5       , 0.2       , 5.        , 3.4000001 ],
       [1.39999998, 0.2       , 4.4000001 , 2.9000001 ],
       [1.5       , 0.1       , 4.9000001 , 3.0999999 ]])
In [168]:
Labels[0:10]
Out[168]:
array([[2.],
       [2.],
       [2.],
       [2.],
       [2.],
       [2.],
       [2.],
       [2.],
       [2.],
       [2.]])
In [ ]:
 
In [178]:
import numpy as np
alphaValues = [0.01, 0.018,0.020, 2, 5, 10]
hiddenSize =  70
# Defining a sigmoid function
def sigmoid(x):
    output = 1 / (1 + np.exp(-x))
    return output


# derivative of sigmoid function
def sigmoidDerivativeFunction(output):
    output = output * (1 - output)
    return output

def forwardProp(X,weight_1,weight_2):
    ##Forward Propogation
    layer_0 = X
    layer_1 = sigmoid(np.dot(layer_0, weight_1))
    layer_2 = sigmoid(np.dot(layer_1, weight_2))
    return layer_0,layer_1,layer_2

def backwardProp(weight_1,weight_2,layer_1,layer_2,alpha,ForwardPropError):
    #Caliculating the error value
    layer_2_Sigma = ForwardPropError * sigmoidDerivativeFunction(layer_2)
    layer_1_Error = layer_2_Sigma.dot(weight_2.T)
    layer_1_Sigma = layer_1_Error * sigmoidDerivativeFunction(layer_1)
    # We are suggesting values .. i.e how much of it is wrong in each layer
    # baically updating the weights withs respective to values and the errors observed
    weight_1_Update = (layer_0.T.dot(layer_1_Sigma))
    weight_2_Update = (layer_1.T.dot(layer_2_Sigma))
    # We are checking what direction the gradient is moving
    weight_1 += alpha * weight_1_Update
    weight_2 += alpha * weight_2_Update
    return weight_1,weight_2
# Some values to construct the nural net work
# Later we will be using the actual data from the iris data set

X = Variables


y = Labels

for alpha in alphaValues:
    print("The current alpha value = ", alpha)
    np.random.seed(1)

    # initializing Random weights

    weight_1 = 2 * np.random.random((4, hiddenSize)) - 1
    weight_2 = 2 * np.random.random((hiddenSize, 1)) - 1

    # checking the weights to see how the gradiants are changing
    for j in range(6000):
        # Forward propogation from one layer to another i.e between layers 0 , 1, 2
        # Caliculating the error value
        layer_0,layer_1,layer_2 = forwardProp(X,weight_1,weight_2)
        ForwardPropError = y - layer_2
        if (j % 1000) == 0:
            print("Error = ", np.mean(np.abs(ForwardPropError)))
        weight_1,weight_2 = backwardProp(weight_1,weight_2,layer_1,layer_2,alpha,ForwardPropError)
    print("weight_1", weight_1)
    print("weight_2", weight_2)
The current alpha value =  0.01
Error =  0.690510991184737
Error =  0.4044019779545721
Error =  0.38845963236829706
Error =  0.38198146420821905
Error =  0.37818268565079627
Error =  0.37552260451579433
weight_1 [[-2.38709228e-01 -1.37411442e+00 -9.99364283e-01 -3.73358574e-01
  -7.06141226e-01 -8.00689314e-01 -6.29554340e-01 -2.58021933e-01
  -1.11310815e-01  6.18499593e-02 -1.63785872e-01  3.32507838e-01
  -6.37193890e-01  9.27020008e-01 -9.49756894e-01  3.58833510e-01
   1.27140570e-01  2.90955436e-01 -7.55573058e-01 -1.08704114e+00
   2.63761301e-01  1.01815615e+00 -1.42180092e-01  6.83787099e-02
   2.37006528e+00  8.22749523e-01 -8.29085560e-01 -9.51009415e-01
  -1.76039414e+00  7.76371953e-01 -8.57485712e-01 -2.49481636e-01
   1.63698978e+00  9.34282657e-02  5.67869827e-01 -3.39416962e-01
   4.53993514e-02  2.20576897e+00 -9.72927629e-01  1.64560045e-01
   2.20548629e+00  6.36003371e-01 -1.33883078e-02  2.09538388e+00
  -7.80166777e-01 -1.88065433e-01  2.42167433e+00 -3.16396728e-01
  -3.44175873e-01 -1.74543679e+00 -1.03934450e+00 -5.82247921e-01
  -3.80908456e+00 -1.46032992e+00 -5.31482355e-02 -3.57847042e+00
   3.96190981e-01 -8.56872696e-01  4.54306404e-02  3.30094002e-01
  -6.85142737e-01  6.69720697e-01  3.52457498e-01 -1.88814918e+00
  -5.85806516e-01 -3.56747740e-01  7.05813685e-01  6.13786572e-03
   9.09063952e-01  1.90584487e-01]
 [ 1.08939265e+00 -1.44600473e+00 -7.21413335e-01  6.21080257e-01
  -2.04390639e-01 -6.69306354e-01  8.54428492e-01 -7.37781014e-02
   7.51605551e-01  4.47992206e-01  7.66132409e-01  2.29552616e-01
   4.92072424e-01  3.37986163e-02 -4.59994605e-01  7.98636312e-01
  -7.35033166e-02  9.89796074e-01  4.11765223e-01 -1.10559006e-01
  -8.57536243e-01  9.06395685e-01  2.56570331e-01  4.96842327e-02
   4.44267570e-01 -5.28342400e-01  8.07050401e-01  1.57379447e-01
  -1.46335361e+00  2.44672042e-01 -3.83250217e-01  2.72280813e-02
   1.10450040e+00 -5.61707208e-01  8.92052420e-01  3.61274562e-01
  -1.05316711e+00  1.49678295e+00  3.82279570e-01  1.06444611e+00
  -1.52965879e-01 -2.68303460e-01  9.94560889e-01  9.99071303e-01
  -8.69617858e-01  4.84964353e-01  1.17507801e+00  1.07860487e+00
   4.10810626e-01 -1.11825805e+00 -9.71152696e-01 -1.33313472e+00
  -3.35764435e+00 -1.34396212e+00  7.08284164e-01 -2.47051810e+00
   1.27500213e-01  6.32136042e-01 -4.75876513e-01 -4.64077336e-01
   3.87991668e-01  1.81475288e+00  1.08837336e-01 -1.66764259e+00
   5.53269202e-01 -6.12084503e-01  1.09174400e+00 -1.84969067e-01
   7.29173731e-01  5.00367455e-01]
 [ 6.00832633e-02 -3.79503138e-01 -8.78319844e-01 -7.33454373e-01
  -9.12637909e-01 -7.03144500e-01 -5.50709405e-01  6.21299282e-02
  -2.23700948e-01 -9.96461708e-01 -8.58919290e-01  9.72673100e-01
   9.47623379e-02 -8.34228375e-01 -5.23472761e-01  4.90327285e-01
  -4.32079942e-01  2.64466348e-01  5.52271827e-01  3.18079110e-01
  -9.18633573e-01  3.50236879e-01 -3.50260336e-03  4.37198682e-01
  -6.86706065e-01 -9.17341103e-01 -8.59702824e-01 -3.67224701e-01
   5.75639922e-01  9.33655293e-02 -1.38111962e-01  8.61482182e-01
  -4.50752363e-01  1.99654824e-01  4.69227244e-02  3.55937369e-01
  -7.11237849e-02 -6.51331967e-01 -9.30678560e-01 -8.13574890e-01
  -5.25752058e-03 -8.30321586e-01  7.23447388e-01 -1.02270456e+00
  -3.83427616e-01  5.06442988e-01 -8.30919373e-01  5.27682303e-02
   1.27671733e-01  6.10781291e-01 -7.63810831e-01 -6.05386807e-01
   2.12324681e+00  8.71913793e-01  7.78773984e-01  2.70909138e+00
  -8.64346327e-01 -4.97301746e-01  3.91432459e-01  8.38279303e-01
   1.01269118e+00 -4.89251170e-01  8.17544605e-01  5.60442939e-01
  -2.98623100e-01 -4.55808650e-01 -1.24338441e-01 -9.37796439e-02
   9.34442866e-01  8.47331552e-01]
 [ 1.21477642e-01  1.95151386e+00 -6.50847902e-01 -7.38442787e-01
  -7.31315195e-01  6.93308839e-02 -9.57637080e-01  8.51553024e-01
   7.19706329e-01 -9.79469067e-01 -6.48826272e-01 -2.91218715e-01
  -7.40668255e-01  6.53381945e-01 -3.30671249e-01  8.73952030e-01
   2.71465481e-01  7.49601296e-01  3.72787138e-01  8.86341673e-01
  -2.12356150e-01  3.47558036e-01  5.90179848e-01 -5.15837123e-01
  -8.31008384e-01  3.85311741e-01 -9.69082340e-01 -7.81638409e-02
   6.80125762e-01  5.68571196e-01 -1.69447468e-01  7.34557682e-01
  -6.54517448e-01 -7.49434387e-01  4.36508967e-01  9.25083855e-02
  -1.02573572e+00 -1.04689923e+00  3.11741465e-01  1.35746209e+00
  -1.66684627e+00  1.36703795e+00 -3.17988181e-01 -2.33520105e-02
   2.78643943e-01  6.13138404e-01 -8.44537377e-01  2.81233474e-01
  -4.20067909e-01  4.77513104e-01  2.99460423e-01  1.32194748e+00
   3.58849067e+00  1.12611326e+00  7.15867941e-01  1.42886200e+00
   6.24459241e-01 -2.04218557e-03 -1.47079340e-01 -2.01794224e-01
  -7.53528800e-01 -8.59022872e-01  2.47836929e-01  9.21131257e-01
   8.48511897e-01  3.01676951e-01 -1.22230984e+00 -1.84965255e-01
  -2.56576111e-01  5.95159152e-01]]
weight_2 [[ 1.54745971]
 [ 2.2253324 ]
 [ 0.32666674]
 [-0.46858446]
 [-0.49439822]
 [ 0.73452719]
 [ 0.04851116]
 [ 1.35256144]
 [ 1.22313531]
 [ 0.45749078]
 [ 0.0181133 ]
 [ 0.76763923]
 [-0.07753645]
 [ 0.80773624]
 [-0.29438755]
 [-0.61741316]
 [-0.70889983]
 [-0.57325242]
 [ 1.69154587]
 [-0.85663829]
 [ 0.48275509]
 [ 0.88999056]
 [ 1.18522157]
 [ 0.46126876]
 [-2.31877323]
 [-0.80086479]
 [-0.30965779]
 [-0.35633771]
 [ 1.92891734]
 [-0.16137188]
 [ 0.90118477]
 [ 1.08534708]
 [-1.50682719]
 [-1.54395839]
 [-0.41437536]
 [ 0.80254497]
 [ 0.00864407]
 [-2.84915122]
 [-0.47578897]
 [ 1.75774012]
 [-2.07579889]
 [ 1.20521692]
 [-0.17325839]
 [-1.77336404]
 [ 0.21578617]
 [ 0.27902669]
 [-2.87476872]
 [ 1.28409357]
 [-0.4793301 ]
 [ 1.51939484]
 [-0.61673377]
 [ 1.02007481]
 [-5.46193693]
 [-1.81097921]
 [ 0.82417099]
 [-4.14658808]
 [-1.02626979]
 [ 0.76829194]
 [ 1.2682596 ]
 [ 0.34087209]
 [ 1.76736861]
 [ 1.66901525]
 [ 0.8988672 ]
 [ 2.5218222 ]
 [-0.61901697]
 [ 0.14856633]
 [ 1.37259149]
 [-0.57921923]
 [ 0.82018305]
 [-0.68246762]]
The current alpha value =  0.018
Error =  0.690510991184737
Error =  0.38999619872223495
Error =  0.3784051180020801
Error =  0.37347722443636083
Error =  0.37023572503010466
Error =  0.36761005053275975
weight_1 [[-0.12455148 -1.58890396 -0.99937084 -0.36301214 -0.70576085 -0.80278117
  -0.63023666 -0.17677811  0.06853835  0.05732659 -0.16398395  0.26904893
  -0.50856893  1.3929053  -0.94879402  0.36297168  0.1491929   0.30740562
  -0.67569003 -1.66545693  0.2332564   1.01585272 -1.65086556 -0.07930724
   2.53534016  0.79805016 -0.82870687 -0.93830456 -1.9461837   0.76151775
  -0.89103005 -0.54676324  1.75941803  0.03697794  0.58222383 -0.48224787
   0.02768427  2.38428692 -0.97080525  0.70650562  2.37854923  0.50754978
  -0.35170899  2.22321376 -0.78506082 -0.38417249  2.59965996 -0.09437806
  -0.33284991 -2.13975255 -1.04184054 -0.64039182 -5.11107402 -2.3403337
  -0.08921126 -4.37463114  0.39791705 -0.88500131  0.06062011  0.03650246
  -0.67486226  1.02598405  0.31337275 -2.08326827 -0.76502946 -0.56844184
   1.26346232 -0.05984496  0.90951873  0.19407636]
 [ 1.17665822 -1.550962   -0.72142573  0.62366291 -0.20426226 -0.67061828
   0.85427195  0.08277302  0.96208849  0.44714083  0.76617808  0.20835204
   0.52127111  0.49895992 -0.4595528   0.79984654 -0.10978132  0.99403901
   0.48830558 -0.74907212 -0.86673934  0.90455195 -0.55672457  0.09611399
   0.51888469 -0.52976928  0.80715754  0.16165718 -1.54360928  0.23954389
  -0.3941364  -0.0490149   1.15598594 -0.66235627  0.89518096  0.06774127
  -1.06049577  1.574531    0.38318789  1.5262023  -0.07758453 -0.5771317
   0.84729358  1.05432454 -0.87258953  0.45195036  1.25375276  1.28428874
   0.33304422 -1.29694029 -0.97131545 -1.36103211 -4.05038821 -2.08401922
   0.69724982 -3.2862367   0.10263085  0.62691491 -0.39148065 -0.53915239
   0.39073807  2.04011245  0.09667547 -1.75335506  0.6661737  -0.72283397
   1.52282519 -0.38263893  0.72876907  0.50132258]
 [-0.06197816 -0.31477901 -0.87818298 -0.71958172 -0.91236245 -0.69702129
  -0.55167473 -0.09891697 -0.4681143  -1.00360928 -0.85943063  0.9139311
   0.24758012 -1.27263273 -0.52436288  0.49526495 -0.4938605   0.28457263
   0.50428778  0.66703329 -0.95710509  0.36322042  0.11466065  0.63829016
  -0.72657883 -0.96086398 -0.85926288 -0.36259171  0.62153256  0.07718804
  -0.16776607  0.45532884 -0.47828709  0.36873726  0.06274546  0.13332175
  -0.08384034 -0.69215583 -0.93231802 -1.43408631 -0.04561118 -1.11658967
   0.56205713 -1.05267139 -0.37111949  0.17104942 -0.87179195 -0.16931407
   0.15927348  0.72216635 -0.77533919 -0.59590129  2.52489827  1.48007834
   0.73376262  3.98395    -0.94710695 -0.54038216  0.38889654  0.54283049
   1.12056131 -0.68216506  0.77480028  0.60777374 -0.61202711 -0.42838634
  -0.40982951  0.02306434  0.94211242  0.85171932]
 [ 0.20243548  2.0873482  -0.65073942 -0.73255937 -0.73127252  0.07513766
  -0.95802623  1.00844015  0.85244825 -0.98255066 -0.64903931 -0.31457
  -0.69694379  0.5132531  -0.33183818  0.87560424  0.34324517  0.75496798
   0.37450186  1.477352   -0.22653659  0.35910154  1.07690464 -0.77756292
  -0.95086527  0.47783466 -0.96891964 -0.08045953  0.81455346  0.56147384
  -0.17462133  0.57134574 -0.74439185 -1.00716439  0.43695962 -0.00578798
  -1.02948404 -1.17545864  0.30950557  1.53464183 -1.77948374  1.35834141
  -0.58154072 -0.12513524  0.29055193  0.55423337 -0.97644124  0.30459399
  -0.48892175  0.7619867   0.29116737  1.36345466  5.17741075  1.70579375
   0.69686868  0.29159675  0.77683117 -0.01651186 -0.16706766 -0.39277721
  -0.95407437 -1.1422919   0.23033183  1.06071147  0.55334693  0.39406852
  -1.77492671 -0.25131039 -0.25065524  0.59676986]]
weight_2 [[ 1.37570633]
 [ 2.49970995]
 [ 0.3267667 ]
 [-0.47394733]
 [-0.49446892]
 [ 0.73668087]
 [ 0.04477001]
 [ 1.5671165 ]
 [ 1.26254651]
 [ 0.45453968]
 [ 0.01058302]
 [ 0.85773978]
 [-0.29049267]
 [ 1.08229175]
 [-0.2934387 ]
 [-0.53402081]
 [-0.98764257]
 [-0.47492804]
 [ 1.82619268]
 [-1.42510747]
 [ 0.472205  ]
 [ 0.96748945]
 [ 1.45636843]
 [ 1.17249587]
 [-2.50136043]
 [-0.98177565]
 [-0.30989471]
 [-0.35705908]
 [ 2.18555887]
 [-0.06177532]
 [ 0.89618531]
 [ 1.26402588]
 [-1.58862285]
 [-1.68108106]
 [-0.29840931]
 [-0.76352585]
 [-0.03859773]
 [-3.11973891]
 [-0.47472283]
 [ 1.95417895]
 [-2.27690686]
 [-1.30179176]
 [ 0.9002734 ]
 [-1.83925356]
 [ 0.2327029 ]
 [ 0.82698343]
 [-3.13528946]
 [ 1.13972089]
 [-0.74338359]
 [ 2.24095248]
 [-0.61274984]
 [ 1.01903401]
 [-5.27224132]
 [-2.26491029]
 [ 0.91240829]
 [-4.11831957]
 [-1.22006144]
 [ 0.75840214]
 [ 1.28245819]
 [ 0.63499415]
 [ 1.85047612]
 [ 1.45940554]
 [ 0.98596129]
 [ 2.8075598 ]
 [-0.26420209]
 [ 0.37977139]
 [ 1.63200434]
 [-0.99607922]
 [ 0.89928705]
 [-0.59949751]]
The current alpha value =  0.02
Error =  0.690510991184737
Error =  0.39038990342665403
Error =  0.37793097287620514
Error =  0.3728308348996243
Error =  0.3693056836247568
Error =  0.366315738781636
weight_1 [[-5.34825766e-02 -1.62306682e+00 -9.99381757e-01 -3.60015153e-01
  -7.05639053e-01 -8.03967265e-01 -6.30391380e-01 -1.64581543e-01
   1.29697258e-01  5.61832807e-02 -1.63992546e-01  2.27473279e-01
  -4.70694583e-01  1.52796245e+00 -9.48386616e-01  3.63792404e-01
   1.58203847e-01  3.10404897e-01 -6.66307168e-01 -1.79573418e+00
   2.25194104e-01  1.01436620e+00 -1.73965367e+00 -6.37850336e-02
   2.56116237e+00  7.96371904e-01 -8.28599353e-01 -9.34067613e-01
  -1.97808801e+00  7.53226700e-01 -8.97283681e-01 -5.32430588e-01
   1.77406614e+00  2.74159372e-02  5.82829890e-01 -4.98322647e-01
   2.51113809e-02  2.41785350e+00 -9.69859274e-01  1.01082905e+00
   2.40465248e+00  7.37978429e-02 -3.38594046e-01  2.23850602e+00
  -7.87422142e-01 -3.69910571e-01  2.63232764e+00  1.81774185e-02
  -3.32186538e-01 -2.20918660e+00 -1.04200263e+00 -6.28799432e-01
  -5.35564865e+00 -2.59902096e+00 -1.04734328e-01 -4.62191178e+00
   4.05614515e-01 -8.90412431e-01  5.88300930e-02  1.15058083e-02
  -6.85045135e-01 -6.37434118e-01  2.95541851e-01 -2.11887423e+00
  -8.09641737e-01 -5.97237243e-01  1.43550399e+00 -6.88292346e-02
   9.09166102e-01  1.94844720e-01]
 [ 1.27191541e+00 -1.56674805e+00 -7.21430513e-01  6.24373220e-01
  -2.04224273e-01 -6.71046723e-01  8.54240470e-01  1.45954442e-01
   1.05694527e+00  4.46937386e-01  7.66201174e-01  1.94850985e-01
   5.18614158e-01  6.76808602e-01 -4.59410958e-01  8.00058136e-01
  -1.43194581e-01  9.94718511e-01  5.42122431e-01 -9.46811038e-01
  -8.69553505e-01  9.03916111e-01 -5.98891003e-01  1.70748300e-01
   5.29503347e-01 -5.48246334e-01  8.07187087e-01  1.62882898e-01
  -1.55577696e+00  2.36832064e-01 -3.95300923e-01 -8.55860416e-03
   1.16036163e+00 -7.25798326e-01  8.94846053e-01 -4.47800323e-03
  -1.06190480e+00  1.58817519e+00  3.83501325e-01  1.78163045e+00
  -6.78188518e-02 -8.03614446e-01  9.05627605e-01  1.05954579e+00
  -8.73387701e-01  4.91154416e-01  1.26733062e+00  1.40483484e+00
   2.99076309e-01 -1.32717998e+00 -9.71287391e-01 -1.35457515e+00
  -4.28288520e+00 -2.37850114e+00  6.92580237e-01 -3.64211743e+00
   7.18498846e-02  6.26267245e-01 -3.44524466e-01 -5.10616786e-01
   4.33288298e-01  9.78019776e-01  9.12816082e-02 -1.76778624e+00
   6.54555445e-01 -7.37564506e-01  1.71596524e+00 -4.40623026e-01
   7.28573806e-01  5.01512402e-01]
 [-1.09048496e-01 -3.00543812e-01 -8.78171768e-01 -7.15556443e-01
  -9.12240256e-01 -6.97327923e-01 -5.51901444e-01 -1.23572465e-01
  -5.30652052e-01 -1.00538282e+00 -8.59503447e-01  8.71470209e-01
   2.95414069e-01 -1.40383791e+00 -5.24196871e-01  4.96381149e-01
  -5.13629539e-01  2.87942687e-01  5.06901785e-01  7.29785383e-01
  -9.65605279e-01  3.64376897e-01  1.49554480e-01  6.39334324e-01
  -7.35848203e-01 -9.79296092e-01 -8.59129817e-01 -3.59005579e-01
   6.32452575e-01  6.81620652e-02 -1.81740551e-01  4.23531082e-01
  -4.84048377e-01  3.99803128e-01  6.36395666e-02  1.57095370e-01
  -8.37399432e-02 -7.02127508e-01 -9.31841407e-01 -1.65773522e+00
  -5.51725927e-02 -1.03248923e+00  5.86871742e-01 -1.06037744e+00
  -3.72318089e-01  1.46154265e-01 -8.81701220e-01 -2.50676001e-01
   1.65104136e-01  7.43897489e-01 -7.76880690e-01 -6.03796197e-01
   2.36828970e+00  1.63103479e+00  7.14281198e-01  4.33993252e+00
  -9.75418351e-01 -5.51512752e-01  3.95315717e-01  5.81012325e-01
   1.16217871e+00 -5.54248395e-01  7.54491963e-01  6.19298147e-01
  -6.58717402e-01 -4.24899106e-01 -4.63936986e-01  4.15584948e-02
   9.42965439e-01  8.52781085e-01]
 [ 1.32000126e-01  2.11257300e+00 -6.50727772e-01 -7.30707357e-01
  -7.31226767e-01  7.53580488e-02 -9.58129367e-01  1.01373916e+00
   8.34223346e-01 -9.83404611e-01 -6.49079332e-01 -3.34296727e-01
  -6.96284869e-01  4.63290547e-01 -3.31871330e-01  8.76093065e-01
   3.78885685e-01  7.55898826e-01  3.34071402e-01  1.66337551e+00
  -2.29995338e-01  3.60572037e-01  1.13792064e+00 -8.42600730e-01
  -9.71038187e-01  5.26703130e-01 -9.68862100e-01 -7.92128672e-02
   8.38361858e-01  5.57925572e-01 -1.82509909e-01  5.84454677e-01
  -7.56975546e-01 -1.03184810e+00  4.36529422e-01  6.82792699e-04
  -1.02897093e+00 -1.20094370e+00  3.09526545e-01  1.33975646e+00
  -1.80078464e+00  8.73251097e-01 -6.84456175e-01 -1.37485129e-01
   2.90566463e-01  5.70403147e-01 -1.00158546e+00  2.11787087e-01
  -4.96276346e-01  8.13129371e-01  2.90025036e-01  1.35846378e+00
   6.00621368e+00  1.97079801e+00  6.88310256e-01  1.29247906e-01
   8.34718656e-01 -2.17255841e-02 -1.91509557e-01 -4.43486191e-01
  -1.04245058e+00 -4.78370222e-01  2.21277131e-01  1.08699210e+00
   5.37160880e-01  4.06759659e-01 -2.04459741e+00 -2.55205504e-01
  -2.49873500e-01  5.97250631e-01]]
weight_2 [[ 1.35046711]
 [ 2.5313671 ]
 [ 0.32677711]
 [-0.47556013]
 [-0.49450915]
 [ 0.73669019]
 [ 0.04377796]
 [ 1.58767721]
 [ 1.26211303]
 [ 0.45376176]
 [ 0.0085168 ]
 [ 0.88642889]
 [-0.37986385]
 [ 1.17539446]
 [-0.29347541]
 [-0.51204079]
 [-1.04551185]
 [-0.44886611]
 [ 1.85212385]
 [-1.54045768]
 [ 0.47000046]
 [ 0.98815412]
 [ 1.58124014]
 [ 1.19829969]
 [-2.51187564]
 [-1.05254457]
 [-0.30997136]
 [-0.35880925]
 [ 2.21800465]
 [-0.03229408]
 [ 0.89277996]
 [ 1.36865688]
 [-1.57817709]
 [-1.70373515]
 [-0.26488216]
 [-0.80946424]
 [-0.05028275]
 [-3.15153109]
 [-0.4748451 ]
 [ 1.96890833]
 [-2.29334466]
 [-0.01720525]
 [ 1.03195364]
 [-1.82785645]
 [ 0.23240966]
 [ 0.96187318]
 [-3.16451288]
 [ 1.10191654]
 [-0.7759782 ]
 [ 2.36902205]
 [-0.61220637]
 [ 0.99452238]
 [-5.43756064]
 [-2.41356482]
 [ 0.9367433 ]
 [-4.29458446]
 [-1.27070963]
 [ 0.75545353]
 [ 1.28486855]
 [ 0.79074057]
 [ 1.89316799]
 [ 0.60445696]
 [ 1.01020398]
 [ 2.84619744]
 [-0.28777324]
 [ 0.40127619]
 [ 1.7913628 ]
 [-1.04794288]
 [ 0.92026215]
 [-0.57765006]]
The current alpha value =  2
Error =  0.690510991184737
Error =  0.6666666666685086
Error =  0.6666666666685086
Error =  0.6666666666685086
Error =  0.6666666666685086
Error =  0.6666666666685087
weight_1 [[-2.01248173 -1.10477751 -0.99902637 -0.39406892 -0.70755648 -0.78512739
  -0.6275964  -0.65197946 -0.36009098  0.07373175 -0.16226664  0.42563106
  -0.62724055  0.86617724 -0.96108829  0.33232028 -0.19770212  0.10734464
  -0.84821185 -0.43138194 -0.39751213  1.60004845 -1.30840727  0.44301734
   2.59915771  0.85590184 -0.83015831 -1.55010577 -0.44436013  0.71659245
  -0.72983784 -0.16431683  0.72590801  0.38254299  0.28948111 -0.36804974
  -0.99467794  2.47809043 -1.00930025  0.68865214  1.02407856 -0.14644624
   0.22581128  2.41036019 -0.78276602 -0.10296566  2.50195977 -0.7733112
  -0.48769066 -1.56994452 -1.44530597  0.44812668  1.65368383 -0.55158921
  -0.01631407  2.31935094  0.17870445 -0.64010024 -1.45837948  0.40349178
  -2.28937229 -2.72230946  0.40116756 -1.74329743 -0.17868829 -0.79237802
   0.15296139 -0.53863709  0.93532862  0.16887198]
 [ 0.02708816 -1.4276238  -0.72133626  0.6158499  -0.20477313 -0.66472295
   0.85489612 -0.4147185   0.4443422   0.45058563  0.76634244  0.25389119
   0.48055881 -0.24931952 -0.4624908   0.79159028 -0.13746545  0.94879526
   0.28495095  0.29911043 -1.14267576  0.99818314 -0.40985407  0.16237993
   0.43576054 -0.5023066   0.80675959  0.08950747 -0.97699326  0.23049566
  -0.33696393  0.05141037  0.74351811 -0.1580835   0.81521376  0.24705608
  -1.48705879  1.50385075  0.37474445  0.99515937 -0.59872759 -0.97427726
   1.17733249  1.17713479 -0.86634778  0.51160483  1.16667215  0.70283883
   0.39101998 -1.01249727 -1.03773469 -1.00523348 -0.23178613 -0.53413048
   0.71929488  1.19288791  0.12772449  0.68678032 -1.43795191 -0.44157101
  -0.33344794 -0.06615672  0.12279638 -1.63723835  1.01559426 -0.86281273
   0.4468399  -0.46049463  0.73431661  0.49462828]
 [ 0.74354983  0.54019468 -0.87748617 -0.76515414 -0.91519548 -0.67562305
  -0.54752116  0.03897152  0.05347532 -0.97730216 -0.85598712  1.16149937
   0.23837239 -0.72687393 -0.55390684  0.44171901 -0.88983591 -0.16149219
   0.79919758  0.88569328 -0.96093541  2.45885699 -0.69620842  0.92110354
   1.06544612 -0.91638323 -0.86138951 -2.93984756  1.24223334 -0.03930862
  -0.07738903  0.9778091  -0.54365765 -0.23624998 -0.41392609  0.49115601
  -0.12689464  0.79746567 -1.03781837  0.82289061 -0.2587706  -0.68373302
  -0.1370855  -1.62516324 -0.44014823  0.60714475 -0.28480764  0.24726239
   0.15072259 -0.17733619 -2.20358265  0.48089126  3.18598359  0.45096578
   0.82898125  3.43594848 -1.11199895 -0.17403559  0.7498051   0.92052539
  -0.45944124 -0.07271185  0.89172467  0.95778293 -1.8525195  -0.25937636
   1.63174092  0.24385103  1.01714909  0.80840509]
 [ 1.27569039  2.7675345  -0.65025691 -0.75443985 -0.73284411  0.08729193
  -0.95602113  0.75039179  0.67860032 -0.97000032 -0.64722651 -0.17338595
  -0.63423304  0.44785921 -0.35110687  0.84673311 -0.05483118  0.49376188
   0.6374609   0.88261503  0.06268601  1.81765362  0.29668963 -0.23709543
   0.50296217  0.2036277  -0.96998362 -1.93416808  0.62026091  0.48830434
  -0.16775027  0.78952211 -0.3336229  -0.79157521  0.1954564   0.22400034
  -0.58131449 -0.04034269  0.23837499  1.63913915 -1.45045434  1.19792921
  -1.12810437 -0.6698362   0.23678818  0.65509865 -0.47367213  0.41570925
  -0.31119601 -0.2650291  -0.70652241  1.80568981  1.64156815  0.36031351
   0.73971827  0.13805369  0.20907708  0.16612562  1.0468066  -0.1615585
  -0.57928172  0.79310842  0.28792709  1.25504173 -0.75749276  0.60873082
   0.67435637  0.2708426  -0.19862278  0.57336927]]
weight_2 [[ 0.65420729]
 [ 2.31820074]
 [ 0.32715664]
 [-0.45552674]
 [-0.4934391 ]
 [ 0.74164738]
 [ 0.05990923]
 [ 2.0110594 ]
 [ 1.48148085]
 [ 0.46569933]
 [ 0.03960756]
 [ 1.75190782]
 [ 0.34146649]
 [-0.17156699]
 [-0.27608163]
 [ 0.42329884]
 [ 0.02124632]
 [ 0.36904035]
 [ 2.28760451]
 [ 0.71120827]
 [ 0.54017138]
 [ 1.2681521 ]
 [ 2.20709285]
 [ 1.00749617]
 [-1.27390535]
 [-0.17122367]
 [-0.30865623]
 [ 1.09709043]
 [ 2.05807825]
 [ 0.82254877]
 [ 0.90488453]
 [ 2.11679876]
 [ 0.49993446]
 [-0.74939313]
 [ 0.49882885]
 [ 1.36794214]
 [-0.1296569 ]
 [-2.68965559]
 [-0.43381202]
 [ 1.64506467]
 [-2.40378922]
 [ 1.71165764]
 [ 0.26155063]
 [-0.81173359]
 [ 0.29581677]
 [ 1.23880488]
 [-2.55360822]
 [ 1.46918956]
 [ 0.47109171]
 [ 2.31593797]
 [-0.16558411]
 [ 1.24168767]
 [ 2.09631983]
 [ 1.82184694]
 [ 1.86359081]
 [ 1.26314204]
 [-0.25275195]
 [ 0.8488457 ]
 [ 1.39314325]
 [ 1.31244073]
 [ 2.40372321]
 [ 0.41119566]
 [ 1.93143181]
 [ 2.72023344]
 [ 2.6861149 ]
 [ 1.86857245]
 [-1.08405813]
 [ 0.82053554]
 [ 1.84067335]
 [ 0.36062875]]
The current alpha value =  5
Error =  0.690510991184737
Error =  0.6666666666666666
Error =  0.6666666666666666
Error =  0.6666666666666666
Error =  0.6666666666666666
Error =  0.6666666666666666
weight_1 [[-4.78227001e+00 -3.42291698e+00 -9.97909045e-01 -3.92170015e-01
  -7.09158879e-01 -7.39834247e-01 -6.27771622e-01 -1.16662975e+00
  -5.90528969e-01  6.78791733e-02 -1.63250152e-01  5.08419142e-01
  -6.81458050e-01  1.03109067e+00 -9.84883516e-01  3.19398161e-01
  -2.46169705e-01  9.22922150e-02 -1.04169008e+00 -1.72759307e-01
  -1.89601402e+00  2.59533640e+00 -2.71129071e+00  5.30575498e-01
   5.36872682e+00  9.55934536e-01 -8.30528420e-01 -2.49242878e+00
  -1.20391549e-01  6.57053645e-01 -6.19635072e-01 -1.74114942e-01
   4.41101823e-01  8.56861349e-01  1.48071808e-01 -3.66670829e-01
  -3.04619764e+00  5.19134906e+00 -1.07811545e+00  9.71197395e-01
   1.09361198e+00 -1.11061229e+00  1.22319638e+00  5.15806206e+00
  -7.66593062e-01 -1.01094716e-01  5.02911291e+00 -1.31411924e+00
  -5.82552442e-01 -2.81494701e+00 -2.17136580e+00  5.83810102e-01
   4.99932523e+00 -6.75611564e-01 -1.55046503e-02  7.13828972e+00
   2.24408309e-01 -5.40436139e-01 -3.91386462e+00  4.09454367e-01
  -4.53043401e+00 -6.54794162e+00  4.19718427e-01 -4.10077992e+00
   9.03418886e-01 -2.08863422e+00 -1.08980471e-01 -1.39125952e+00
   1.00453728e+00  1.62514839e-01]
 [-1.14248523e+00 -2.48148353e+00 -7.21169690e-01  6.17450894e-01
  -2.04963327e-01 -6.57869960e-01  8.54714562e-01 -5.80093486e-01
   3.58419487e-01  4.48470117e-01  7.65937826e-01  2.63711345e-01
   4.48569760e-01 -1.69993861e-01 -4.66010668e-01  7.91317056e-01
  -1.27937194e-01  9.77468030e-01  2.22052996e-01  3.82688906e-01
  -1.70092733e+00  1.14699009e+00 -8.74371587e-01  1.70780977e-01
   1.36499093e+00 -4.66847452e-01  8.06760413e-01  2.73021554e-03
  -9.51094133e-01  2.24804406e-01 -3.22344525e-01  4.73516235e-02
   7.00969102e-01  3.29818772e-02  8.12429070e-01  2.47559976e-01
  -2.26511070e+00  2.47131517e+00  3.64170374e-01  9.95929867e-01
  -5.13840872e-01 -1.34710030e+00  1.64554489e+00  2.35238234e+00
  -8.63869958e-01  5.12622925e-01  2.15505180e+00  4.88023846e-01
   3.42975751e-01 -1.40405607e+00 -1.15397713e+00 -1.09171665e+00
   8.35615216e-01 -5.73958953e-01  7.18153346e-01  2.86572657e+00
   1.60845281e-01  6.90858171e-01 -2.46739950e+00 -4.41478562e-01
  -1.09089765e+00 -1.57417905e+00  1.23900304e-01 -2.64903729e+00
   1.63708763e+00 -1.35595463e+00  1.95784168e-01 -8.14818349e-01
   7.45165970e-01  4.95205768e-01]
 [ 1.69015433e+00  2.44112139e+00 -8.73468483e-01 -7.76915721e-01
  -9.21644340e-01 -5.11540006e-01 -5.45930926e-01 -5.41536728e-01
  -4.54614452e-02 -9.80923328e-01 -8.55890630e-01  1.50191943e+00
   3.91629693e-01 -9.27064692e-01 -6.41744343e-01  3.72819966e-01
  -1.31087822e+00 -6.47807121e-01  5.87934449e-01  1.17374679e+00
  -1.62188179e+00  6.16583334e+00 -2.10038821e+00  1.31581616e+00
   3.69324113e+00 -8.46686783e-01 -8.63540206e-01 -7.30865422e+00
   2.78659500e+00 -3.04825825e-01  3.54440223e-01  9.78674302e-01
  -1.59837925e+00 -2.31048801e-01 -1.18765938e+00  4.91887271e-01
  -8.24935286e-01  2.69890551e+00 -1.29355044e+00  2.44697393e+00
  -1.03608052e+00 -8.39854214e-01 -1.10098020e+00 -2.76251813e+00
  -3.81315881e-01  6.03598199e-01  2.07678046e-01  1.99774898e-01
   3.02795834e-01 -1.71776438e+00 -4.79884694e+00  2.50434487e+00
   7.25976109e+00  3.10882255e-01  8.49005562e-01  7.29395499e+00
  -1.32185209e+00  3.61825010e-01  1.52417859e+00  9.54264518e-01
  -2.49913147e+00 -3.51739184e-01  9.82492634e-01  1.96976067e+00
  -4.30132189e+00 -6.06412823e-01  3.76642084e+00  4.60984508e-01
   1.26432844e+00  7.64812410e-01]
 [ 3.50459934e+00  5.52904882e+00 -6.47509271e-01 -7.65088183e-01
  -7.37347743e-01  2.01243319e-01 -9.54627236e-01  5.32069469e-01
   7.15154914e-01 -9.70057739e-01 -6.46655034e-01  7.03444065e-02
  -4.78573095e-01  1.91175874e-01 -4.11977145e-01  7.96510318e-01
  -3.83120487e-01  9.79088116e-02  5.59449107e-01  9.90360618e-01
   2.77074222e-01  4.40509361e+00 -1.54086691e-01  5.01048651e-02
   1.28664486e+00  2.11738274e-01 -9.71558886e-01 -5.11586442e+00
   1.74962323e+00  2.98679268e-01  1.34889938e-01  7.95139164e-01
  -1.06762866e+00 -1.03096878e+00 -3.75146471e-01  2.23907547e-01
  -1.15014040e-01  1.38562227e-01  5.87309691e-02  2.84204254e+00
  -2.12734258e+00  1.56454575e+00 -2.45000179e+00 -3.09594138e+00
   2.77822145e-01  6.51209220e-01 -1.40831484e+00  6.55045218e-01
  -1.34718752e-01 -9.23072780e-01 -2.51637131e+00  3.43928301e+00
   3.33867382e+00  3.06612866e-01  7.55857387e-01  8.77091225e-01
   1.03263223e-02  5.62692356e-01  2.97070866e+00 -1.36330301e-01
  -1.15264304e+00  2.53061922e+00  3.54059615e-01  3.34686340e+00
  -3.31513813e+00  9.88424416e-01  2.59018127e+00  8.97003792e-01
  -2.65956769e-02  5.40506774e-01]]
weight_2 [[ 0.49552351]
 [ 4.58397667]
 [ 0.32973216]
 [-0.44944163]
 [-0.49069786]
 [ 0.78942462]
 [ 0.06662914]
 [ 4.1211654 ]
 [ 3.48623711]
 [ 0.46482075]
 [ 0.04198401]
 [ 3.56711853]
 [ 0.64709232]
 [-0.32604668]
 [-0.21827081]
 [ 2.35361979]
 [ 0.41934325]
 [ 2.1837233 ]
 [ 4.27056062]
 [ 2.73318285]
 [ 0.41485236]
 [ 2.04549604]
 [ 4.95249238]
 [ 2.31025792]
 [-2.16767697]
 [-0.32869912]
 [-0.30715672]
 [ 3.56760619]
 [ 4.86766074]
 [ 2.61956313]
 [ 1.01329468]
 [ 4.06309106]
 [ 1.97848167]
 [-0.70615665]
 [ 2.16818437]
 [ 3.42110349]
 [-1.00989925]
 [-5.84872157]
 [-0.32863072]
 [ 3.05764676]
 [-5.75701907]
 [ 3.92908893]
 [ 1.45287834]
 [-0.83523523]
 [ 0.69197088]
 [ 3.16558995]
 [-5.34203476]
 [ 3.30755556]
 [ 1.04569904]
 [ 5.32743287]
 [ 0.65240312]
 [ 2.34963645]
 [ 6.07465288]
 [ 4.49656231]
 [ 3.80308967]
 [ 4.59086454]
 [-0.10496726]
 [ 1.00334717]
 [ 2.44872945]
 [ 3.16578076]
 [ 4.90948321]
 [-0.32142883]
 [ 3.84935925]
 [ 5.73823745]
 [ 7.91905705]
 [ 4.21751813]
 [-3.32069556]
 [ 1.72061662]
 [ 3.70283831]
 [ 2.29785893]]
The current alpha value =  10
Error =  0.690510991184737
Error =  0.6666666666666666
Error =  0.6666666666666666
Error =  0.6666666666666666
Error =  0.6666666666666666
Error =  0.6666666666666666
weight_1 [[-9.39858402e+00 -7.28648295e+00 -9.96046840e-01 -3.89005176e-01
  -7.11829540e-01 -6.64345684e-01 -6.28063667e-01 -2.02438096e+00
  -9.74592886e-01  5.81248785e-02 -1.64889334e-01  6.46399282e-01
  -7.71820599e-01  1.30594646e+00 -1.02454222e+00  2.97861303e-01
  -3.26949015e-01  6.72047731e-02 -1.36415403e+00  2.58278408e-01
  -4.39351718e+00  4.25414965e+00 -5.04942979e+00  6.76505764e-01
   9.98467533e+00  1.12265575e+00 -8.31145262e-01 -4.06296713e+00
   4.19556062e-01  5.57822282e-01 -4.35963812e-01 -1.90445134e-01
  -3.35754134e-02  1.64739213e+00 -8.76106122e-02 -3.64372920e-01
  -6.46539714e+00  9.71344678e+00 -1.19280745e+00  1.44210616e+00
   1.20950179e+00 -2.71755589e+00  2.88550477e+00  9.73756546e+00
  -7.39638137e-01 -9.79764848e-02  9.24103482e+00 -2.21546678e+00
  -7.40655562e-01 -4.88995116e+00 -3.38146552e+00  8.09949139e-01
   1.05753942e+01 -8.82316446e-01 -1.41556191e-02  1.51698544e+01
   3.00581406e-01 -3.74329428e-01 -8.00634032e+00  4.19392013e-01
  -8.26553688e+00 -1.29239952e+01  4.50636539e-01 -8.02991838e+00
   2.70693085e+00 -4.24906125e+00 -5.45550232e-01 -2.81229727e+00
   1.11988505e+00  1.51919597e-01]
 [-3.09177429e+00 -4.23791647e+00 -7.20892074e-01  6.20119211e-01
  -2.05280329e-01 -6.46448314e-01  8.54411963e-01 -8.55718692e-01
   2.15214768e-01  4.44944264e-01  7.65263470e-01  2.80078276e-01
   3.95254653e-01 -3.77844051e-02 -4.71877120e-01  7.90861677e-01
  -1.12056767e-01  1.02525597e+00  1.17222997e-01  5.21986372e-01
  -2.63134660e+00  1.39500165e+00 -1.64856744e+00  1.84782726e-01
   2.91370826e+00 -4.07748864e-01  8.06761785e-01 -1.41898542e-01
  -9.07928920e-01  2.15318986e-01 -2.97978853e-01  4.05870425e-02
   6.30054006e-01  3.51424234e-01  8.07787837e-01  2.48399721e-01
  -3.56186389e+00  4.08375587e+00  3.46546913e-01  9.97214034e-01
  -3.72362760e-01 -1.96847211e+00  2.42589885e+00  4.31112836e+00
  -8.59740261e-01  5.14319745e-01  3.80235122e+00  1.29998621e-01
   2.62901985e-01 -2.05665407e+00 -1.34771453e+00 -1.23585527e+00
   2.61461746e+00 -6.40340041e-01  7.16250794e-01  5.65379102e+00
   2.16046604e-01  6.97654558e-01 -4.18314564e+00 -4.41324483e-01
  -2.35331385e+00 -4.08754960e+00  1.25740169e-01 -4.33536916e+00
   2.67290991e+00 -2.17785781e+00 -2.22642055e-01 -1.40535799e+00
   7.63248232e-01  4.96168250e-01]
 [ 3.26782819e+00  5.60933232e+00 -8.66772346e-01 -7.96518353e-01
  -9.32392438e-01 -2.38068270e-01 -5.43280530e-01 -1.50905142e+00
  -2.10356854e-01 -9.86958617e-01 -8.55729820e-01  2.06928620e+00
   6.47058461e-01 -1.26071585e+00 -7.88140176e-01  2.57988224e-01
  -2.01261541e+00 -1.45833210e+00  2.35828920e-01  1.65383598e+00
  -2.72345911e+00  1.23441272e+01 -4.44068786e+00  1.97367053e+00
   8.07289946e+00 -7.30525971e-01 -8.67124700e-01 -1.45899987e+01
   5.36053109e+00 -7.47354525e-01  1.07415563e+00  9.80116295e-01
  -3.35624894e+00 -2.22379947e-01 -2.47721521e+00  4.93105681e-01
  -1.98833636e+00  5.86797190e+00 -1.71977055e+00  5.15377946e+00
  -2.33159606e+00 -1.10005645e+00 -2.70747152e+00 -4.65810922e+00
  -2.83261960e-01  5.97687271e-01  1.02848753e+00  1.20628033e-01
   5.56251049e-01 -4.28514471e+00 -9.12428742e+00  5.87676755e+00
   1.40493903e+01  7.74084519e-02  8.82379418e-01  1.37239659e+01
  -1.67160733e+00  1.25492585e+00  2.81480046e+00  1.01049640e+00
  -5.89861517e+00 -8.16784745e-01  1.13377257e+00  3.65638892e+00
  -8.38265921e+00 -1.18480698e+00  7.32422072e+00  8.22873174e-01
   1.67629403e+00  6.92157949e-01]
 [ 7.21944746e+00  1.01315726e+01 -6.42929874e-01 -7.82835405e-01
  -7.44853801e-01  3.91162308e-01 -9.52304083e-01  1.68198515e-01
   7.76078886e-01 -9.70153439e-01 -6.45702579e-01  4.76561664e-01
  -2.19139879e-01 -2.36629635e-01 -5.13427595e-01  7.12805671e-01
  -9.30269334e-01 -5.61846346e-01  4.29429323e-01  1.16993660e+00
   6.34387913e-01  8.71749359e+00 -9.05380564e-01  5.28772027e-01
   2.59278267e+00  2.25255932e-01 -9.74184324e-01 -1.04186917e+01
   3.63189377e+00 -1.73625212e-02  6.39290270e-01  8.04500910e-01
  -2.29097176e+00 -1.42995797e+00 -1.32615141e+00  2.23752741e-01
   6.62153376e-01  4.36737095e-01 -2.40675735e-01  4.84688152e+00
  -3.25548922e+00  2.17557321e+00 -4.65316420e+00 -7.13944984e+00
   3.46212088e-01  6.44726823e-01 -2.96605268e+00  1.05393804e+00
   1.59409932e-01 -2.01981224e+00 -5.53278615e+00  6.16193835e+00
   6.16718326e+00  2.17111235e-01  7.82755914e-01  2.10882046e+00
  -3.20924937e-01  1.22363684e+00  6.17721182e+00 -9.42833019e-02
  -2.10824525e+00  5.42647054e+00  4.64280493e-01  6.83323226e+00
  -7.57788041e+00  1.62124705e+00  5.78322276e+00  1.94060557e+00
   2.60116167e-01  4.85735940e-01]]
weight_2 [[  0.23105045]
 [  8.36026942]
 [  0.3340247 ]
 [ -0.43929978]
 [ -0.48612913]
 [  0.86905335]
 [  0.07782899]
 [  7.63800864]
 [  6.8274972 ]
 [  0.46335645]
 [  0.04594477]
 [  6.59246925]
 [  1.15646865]
 [ -0.58351311]
 [ -0.12191943]
 [  5.57082087]
 [  1.08283814]
 [  5.20819444]
 [  7.57548702]
 [  6.10314   ]
 [  0.20598733]
 [  3.34106879]
 [  9.52815826]
 [  4.48152701]
 [ -3.6572968 ]
 [ -0.59115829]
 [ -0.30465754]
 [  7.68513247]
 [  9.55029773]
 [  5.61458659]
 [  1.19397825]
 [  7.30691107]
 [  4.44272675]
 [ -0.6340959 ]
 [  4.95044328]
 [  6.84303864]
 [ -2.47696984]
 [-11.11383201]
 [ -0.15332855]
 [  5.41194978]
 [-11.34573557]
 [  7.62480772]
 [  3.4384244 ]
 [ -0.87440497]
 [  1.35222772]
 [  6.37689793]
 [ -9.98941282]
 [  6.37149866]
 [  2.00337784]
 [ 10.34659105]
 [  2.01571515]
 [  4.19621727]
 [ 12.70520748]
 [  8.95442098]
 [  7.03558728]
 [ 10.13706823]
 [  0.14134056]
 [  1.26084959]
 [  4.20803969]
 [  6.25468034]
 [  9.08574987]
 [ -1.54246964]
 [  7.04590451]
 [ 10.76824401]
 [ 16.6406273 ]
 [  8.13242759]
 [ -7.04842509]
 [  3.22075161]
 [  6.80644609]
 [  5.52657543]]
In [ ]:
 
In [98]:
 
DataFrame[Species_category: double]
In [97]:
 
[6.50665504e-02 1.82197426e-01 6.90731850e-01 3.02682520e-04
 5.29099341e-01 4.06257450e-01 6.06375248e-01 7.05381419e-01
 9.37774781e-01 9.80708246e-01]
In [ ]: