Re: [scikit-learn] CLUSTER ANALYSIS AND THE SEARCH OF A SAMPLE MODE
of course. Here it is Il giorno lun 18 set 2023 alle ore 18:10 Jaime Lopez ha scritto: > Hi, > > Same error, maybe it could be related to the database I got from github > (iris.xlsx), could you share yours?. > > [image: image.png] > > JL > > On Mon, Sep 18, 2023 at 1:57 AM Ulderico Santarelli < > ulderico.santare...@gmail.com> wrote: > >> *I think it better to send you the script in its integrity. I ran now and >> it works. * >> *about work it is* >> work >> array([[ 5.63011247], >>[-2.31453939], >>[22.23122848], >>[15.37678101]]) >> np.shape(work) >> (4, 1) >> >> *my best regards. * >> *Ulderico.* >> >> _ >> import numpy as np >> import pandas as pd >> dataraw = pd.read_excel("C:\Pyth\iris.xlsx") >> #standardize data --- dataraw is a DataFrame >> #locate data in the DataFrame >> datar = dataraw.iloc[:,1:5] >> means = datar.mean(axis = 0) >> stdev = datar.std(axis = 0) >> data = (datar-means)/stdev >> #keep just quantitative variables >> #CENTRALITY INDEX >> scalar = pd.merge(data, data, how = 'cross') >> point1 = scalar.loc[:, 'sepal length _x':'petal width _x'] >> point2 = scalar.loc[:, 'sepal length _y':'petal width _y'] >> apoint1 = point1.to_numpy(dtype = float) >> apoint2 = point2.to_numpy(dtype = float) >> delta = (apoint1 - apoint2) >> force = 0 >> if delta.any() != 0: >> force = np.exp(-abs(delta)) >> sig = np.sign(delta) >> sforce = sig*force >> dsforce = pd.DataFrame(sforce) >> #dsforce.to_excel('C:\Pyth\dsforce.xlsx') >> arr = np.ones((150, 1),) >> sforcet = sforce.T >> sum_force =np.zeros((1, 4),) #do not use empty arrays >> start = 0 >> end = 150 >> for i in range(150): >> s_forcet = sforcet[:, start:end] >> work = np.matmul(s_forcet, arr) >> sum_force =np.concatenate((sum_force, work.reshape(1, 4)), axis = 0) >> start = end >> end +=150 >> sumforce = sum_force[1:, :] >> dsumforce = pd.DataFrame(sumforce) >> dsumforce.to_excel('C:\Pyth\sumforce_sqc.xlsx') >> sum_force_square = sumforce**2 >> ssT = np.ones((4, 1),) >> T_w_ = np.sqrt(np.matmul(sum_force_square, ssT)) >> dT_w_ = pd.DataFrame(T_w_, ) >> dT_w_.to_excel('C:\Pyth\T_w_.xlsx') >> >> Il giorno dom 17 set 2023 alle ore 18:14 Jaime Lopez >> ha scritto: >> >>> Hi there, >>> >>> I got interested in your project, but I found this error from the >>> beginning (see attached image). >>> The work array cannot be reshaped to (1,4), cause it has shape (2,1), >>> any suggestions? >>> >>> JL >>> >>> [image: image.png] >>> >>> On Thu, Sep 14, 2023 at 11:29 AM Ulderico Santarelli < >>> ulderico.santare...@gmail.com> wrote: >>> *I am an old guy who started programming around the seventies of the last century* with ASSEMBLER 360, then FORTRAN, PL1, APL, IBM APPLICATION SYSTEM and, last, the marvelous SAS. Having heard around about the powerful, flexible, functionally complete PYTHON UNIVERSE”, encompassing an advanced Object-Oriented Language and a very wide family of packages, I decided to run an exercise about a problem I've been tackling since my youth (have a look at the Bibliography). I succeeded in completing it in a few days and I'm attaching my solution to the problem of finding the points in a sample that are "central" in a surrounding topological neighborhood. They are eligible as centroids for a Cluster Analysis after the aggregation of "too near points'. The solution is based on the search of potential wells in a suitable potential field, similar to the one all of us studied in high school. Therefore, too near points may be in the same potential well. No more words, have a look at the attachment. My coding is that of a beginner. I'm sure everybody would find more efficient coding. As a comment: I started studying Python around May 15th 2023. My best regards. Ulderico Santarelli. ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn >>> >>> >>> -- >>> >>> *Jaime Lopez Carvajal* >>> ___ >>> scikit-learn mailing list >>> scikit-learn@python.org >>> https://mail.python.org/mailman/listinfo/scikit-learn >>> >> ___ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> > > > -- > > *Jaime Lopez Carvajal* > ___ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > iris.xlsx Description: MS-Excel 2007 spreadsheet ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
Re: [scikit-learn] CLUSTER ANALYSIS AND THE SEARCH OF A SAMPLE MODE
Hi, Same error, maybe it could be related to the database I got from github (iris.xlsx), could you share yours?. [image: image.png] JL On Mon, Sep 18, 2023 at 1:57 AM Ulderico Santarelli < ulderico.santare...@gmail.com> wrote: > *I think it better to send you the script in its integrity. I ran now and > it works. * > *about work it is* > work > array([[ 5.63011247], >[-2.31453939], >[22.23122848], >[15.37678101]]) > np.shape(work) > (4, 1) > > *my best regards. * > *Ulderico.* > > _ > import numpy as np > import pandas as pd > dataraw = pd.read_excel("C:\Pyth\iris.xlsx") > #standardize data --- dataraw is a DataFrame > #locate data in the DataFrame > datar = dataraw.iloc[:,1:5] > means = datar.mean(axis = 0) > stdev = datar.std(axis = 0) > data = (datar-means)/stdev > #keep just quantitative variables > #CENTRALITY INDEX > scalar = pd.merge(data, data, how = 'cross') > point1 = scalar.loc[:, 'sepal length _x':'petal width _x'] > point2 = scalar.loc[:, 'sepal length _y':'petal width _y'] > apoint1 = point1.to_numpy(dtype = float) > apoint2 = point2.to_numpy(dtype = float) > delta = (apoint1 - apoint2) > force = 0 > if delta.any() != 0: > force = np.exp(-abs(delta)) > sig = np.sign(delta) > sforce = sig*force > dsforce = pd.DataFrame(sforce) > #dsforce.to_excel('C:\Pyth\dsforce.xlsx') > arr = np.ones((150, 1),) > sforcet = sforce.T > sum_force =np.zeros((1, 4),) #do not use empty arrays > start = 0 > end = 150 > for i in range(150): > s_forcet = sforcet[:, start:end] > work = np.matmul(s_forcet, arr) > sum_force =np.concatenate((sum_force, work.reshape(1, 4)), axis = 0) > start = end > end +=150 > sumforce = sum_force[1:, :] > dsumforce = pd.DataFrame(sumforce) > dsumforce.to_excel('C:\Pyth\sumforce_sqc.xlsx') > sum_force_square = sumforce**2 > ssT = np.ones((4, 1),) > T_w_ = np.sqrt(np.matmul(sum_force_square, ssT)) > dT_w_ = pd.DataFrame(T_w_, ) > dT_w_.to_excel('C:\Pyth\T_w_.xlsx') > > Il giorno dom 17 set 2023 alle ore 18:14 Jaime Lopez > ha scritto: > >> Hi there, >> >> I got interested in your project, but I found this error from the >> beginning (see attached image). >> The work array cannot be reshaped to (1,4), cause it has shape (2,1), any >> suggestions? >> >> JL >> >> [image: image.png] >> >> On Thu, Sep 14, 2023 at 11:29 AM Ulderico Santarelli < >> ulderico.santare...@gmail.com> wrote: >> >>> *I am an old guy who started programming around the seventies of >>> the last century* with ASSEMBLER 360, then FORTRAN, PL1, APL, IBM >>> APPLICATION SYSTEM and, last, the marvelous SAS. Having heard around about >>> the powerful, flexible, functionally complete PYTHON UNIVERSE”, >>> encompassing an advanced Object-Oriented Language and a very wide family of >>> packages, I decided to run an exercise about a problem I've been >>> tackling since my youth (have a look at the Bibliography). I succeeded in >>> completing it in a few days and I'm attaching my solution to the problem of >>> finding the points in a sample that are "central" in a surrounding >>> topological neighborhood. They are eligible as centroids for a Cluster >>> Analysis after the aggregation of "too near points'. The solution is based >>> on the search of potential wells in a suitable potential field, similar to >>> the one all of us studied in high school. Therefore, too near points may be >>> in the same potential well. >>> No more words, have a look at the attachment. >>> My coding is that of a beginner. I'm sure everybody would find more >>> efficient coding. As a comment: I started studying Python around May 15th >>> 2023. >>> My best regards. >>> Ulderico Santarelli. >>> ___ >>> scikit-learn mailing list >>> scikit-learn@python.org >>> https://mail.python.org/mailman/listinfo/scikit-learn >>> >> >> >> -- >> >> *Jaime Lopez Carvajal* >> ___ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> > ___ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > -- *Jaime Lopez Carvajal* ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
Re: [scikit-learn] CLUSTER ANALYSIS AND THE SEARCH OF A SAMPLE MODE
in addition, *the distance I'm using is not a dogma*. It is meant to avoid the "black holes syndrome" that would emerge using the sheer Newtonian distance when by chance two points are too near. When the distance is 0, exp(-|w-x|) would be 1 and is set to 0. I tried also exp{-|w-x|^2) but changes are not significant. Ulderico. Il giorno dom 17 set 2023 alle ore 18:14 Jaime Lopez ha scritto: > Hi there, > > I got interested in your project, but I found this error from the > beginning (see attached image). > The work array cannot be reshaped to (1,4), cause it has shape (2,1), any > suggestions? > > JL > > [image: image.png] > > On Thu, Sep 14, 2023 at 11:29 AM Ulderico Santarelli < > ulderico.santare...@gmail.com> wrote: > >> *I am an old guy who started programming around the seventies of >> the last century* with ASSEMBLER 360, then FORTRAN, PL1, APL, IBM >> APPLICATION SYSTEM and, last, the marvelous SAS. Having heard around about >> the powerful, flexible, functionally complete PYTHON UNIVERSE”, >> encompassing an advanced Object-Oriented Language and a very wide family of >> packages, I decided to run an exercise about a problem I've been >> tackling since my youth (have a look at the Bibliography). I succeeded in >> completing it in a few days and I'm attaching my solution to the problem of >> finding the points in a sample that are "central" in a surrounding >> topological neighborhood. They are eligible as centroids for a Cluster >> Analysis after the aggregation of "too near points'. The solution is based >> on the search of potential wells in a suitable potential field, similar to >> the one all of us studied in high school. Therefore, too near points may be >> in the same potential well. >> No more words, have a look at the attachment. >> My coding is that of a beginner. I'm sure everybody would find more >> efficient coding. As a comment: I started studying Python around May 15th >> 2023. >> My best regards. >> Ulderico Santarelli. >> ___ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> > > > -- > > *Jaime Lopez Carvajal* > ___ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
Re: [scikit-learn] CLUSTER ANALYSIS AND THE SEARCH OF A SAMPLE MODE
*I think it better to send you the script in its integrity. I ran now and it works. * *about work it is* work array([[ 5.63011247], [-2.31453939], [22.23122848], [15.37678101]]) np.shape(work) (4, 1) *my best regards. * *Ulderico.* _ import numpy as np import pandas as pd dataraw = pd.read_excel("C:\Pyth\iris.xlsx") #standardize data --- dataraw is a DataFrame #locate data in the DataFrame datar = dataraw.iloc[:,1:5] means = datar.mean(axis = 0) stdev = datar.std(axis = 0) data = (datar-means)/stdev #keep just quantitative variables #CENTRALITY INDEX scalar = pd.merge(data, data, how = 'cross') point1 = scalar.loc[:, 'sepal length _x':'petal width _x'] point2 = scalar.loc[:, 'sepal length _y':'petal width _y'] apoint1 = point1.to_numpy(dtype = float) apoint2 = point2.to_numpy(dtype = float) delta = (apoint1 - apoint2) force = 0 if delta.any() != 0: force = np.exp(-abs(delta)) sig = np.sign(delta) sforce = sig*force dsforce = pd.DataFrame(sforce) #dsforce.to_excel('C:\Pyth\dsforce.xlsx') arr = np.ones((150, 1),) sforcet = sforce.T sum_force =np.zeros((1, 4),) #do not use empty arrays start = 0 end = 150 for i in range(150): s_forcet = sforcet[:, start:end] work = np.matmul(s_forcet, arr) sum_force =np.concatenate((sum_force, work.reshape(1, 4)), axis = 0) start = end end +=150 sumforce = sum_force[1:, :] dsumforce = pd.DataFrame(sumforce) dsumforce.to_excel('C:\Pyth\sumforce_sqc.xlsx') sum_force_square = sumforce**2 ssT = np.ones((4, 1),) T_w_ = np.sqrt(np.matmul(sum_force_square, ssT)) dT_w_ = pd.DataFrame(T_w_, ) dT_w_.to_excel('C:\Pyth\T_w_.xlsx') Il giorno dom 17 set 2023 alle ore 18:14 Jaime Lopez ha scritto: > Hi there, > > I got interested in your project, but I found this error from the > beginning (see attached image). > The work array cannot be reshaped to (1,4), cause it has shape (2,1), any > suggestions? > > JL > > [image: image.png] > > On Thu, Sep 14, 2023 at 11:29 AM Ulderico Santarelli < > ulderico.santare...@gmail.com> wrote: > >> *I am an old guy who started programming around the seventies of >> the last century* with ASSEMBLER 360, then FORTRAN, PL1, APL, IBM >> APPLICATION SYSTEM and, last, the marvelous SAS. Having heard around about >> the powerful, flexible, functionally complete PYTHON UNIVERSE”, >> encompassing an advanced Object-Oriented Language and a very wide family of >> packages, I decided to run an exercise about a problem I've been >> tackling since my youth (have a look at the Bibliography). I succeeded in >> completing it in a few days and I'm attaching my solution to the problem of >> finding the points in a sample that are "central" in a surrounding >> topological neighborhood. They are eligible as centroids for a Cluster >> Analysis after the aggregation of "too near points'. The solution is based >> on the search of potential wells in a suitable potential field, similar to >> the one all of us studied in high school. Therefore, too near points may be >> in the same potential well. >> No more words, have a look at the attachment. >> My coding is that of a beginner. I'm sure everybody would find more >> efficient coding. As a comment: I started studying Python around May 15th >> 2023. >> My best regards. >> Ulderico Santarelli. >> ___ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> > > > -- > > *Jaime Lopez Carvajal* > ___ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
Re: [scikit-learn] CLUSTER ANALYSIS AND THE SEARCH OF A SAMPLE MODE
I'm going to have a look at this. Thank you for your comment. Il giorno dom 17 set 2023 alle ore 18:14 Jaime Lopez ha scritto: > Hi there, > > I got interested in your project, but I found this error from the > beginning (see attached image). > The work array cannot be reshaped to (1,4), cause it has shape (2,1), any > suggestions? > > JL > > [image: image.png] > > On Thu, Sep 14, 2023 at 11:29 AM Ulderico Santarelli < > ulderico.santare...@gmail.com> wrote: > >> *I am an old guy who started programming around the seventies of >> the last century* with ASSEMBLER 360, then FORTRAN, PL1, APL, IBM >> APPLICATION SYSTEM and, last, the marvelous SAS. Having heard around about >> the powerful, flexible, functionally complete PYTHON UNIVERSE”, >> encompassing an advanced Object-Oriented Language and a very wide family of >> packages, I decided to run an exercise about a problem I've been >> tackling since my youth (have a look at the Bibliography). I succeeded in >> completing it in a few days and I'm attaching my solution to the problem of >> finding the points in a sample that are "central" in a surrounding >> topological neighborhood. They are eligible as centroids for a Cluster >> Analysis after the aggregation of "too near points'. The solution is based >> on the search of potential wells in a suitable potential field, similar to >> the one all of us studied in high school. Therefore, too near points may be >> in the same potential well. >> No more words, have a look at the attachment. >> My coding is that of a beginner. I'm sure everybody would find more >> efficient coding. As a comment: I started studying Python around May 15th >> 2023. >> My best regards. >> Ulderico Santarelli. >> ___ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> > > > -- > > *Jaime Lopez Carvajal* > ___ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
Re: [scikit-learn] CLUSTER ANALYSIS AND THE SEARCH OF A SAMPLE MODE
Hi there, I got interested in your project, but I found this error from the beginning (see attached image). The work array cannot be reshaped to (1,4), cause it has shape (2,1), any suggestions? JL [image: image.png] On Thu, Sep 14, 2023 at 11:29 AM Ulderico Santarelli < ulderico.santare...@gmail.com> wrote: > *I am an old guy who started programming around the seventies of > the last century* with ASSEMBLER 360, then FORTRAN, PL1, APL, IBM > APPLICATION SYSTEM and, last, the marvelous SAS. Having heard around about > the powerful, flexible, functionally complete PYTHON UNIVERSE”, > encompassing an advanced Object-Oriented Language and a very wide family of > packages, I decided to run an exercise about a problem I've been tackling > since my youth (have a look at the Bibliography). I succeeded in completing > it in a few days and I'm attaching my solution to the problem of finding > the points in a sample that are "central" in a surrounding topological > neighborhood. They are eligible as centroids for a Cluster Analysis after > the aggregation of "too near points'. The solution is based on the search > of potential wells in a suitable potential field, similar to the one all of > us studied in high school. Therefore, too near points may be in the same > potential well. > No more words, have a look at the attachment. > My coding is that of a beginner. I'm sure everybody would find more > efficient coding. As a comment: I started studying Python around May 15th > 2023. > My best regards. > Ulderico Santarelli. > ___ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > -- *Jaime Lopez Carvajal* ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
[scikit-learn] CLUSTER ANALYSIS AND THE SEARCH OF A SAMPLE MODE
*I am an old guy who started programming around the seventies of the last century* with ASSEMBLER 360, then FORTRAN, PL1, APL, IBM APPLICATION SYSTEM and, last, the marvelous SAS. Having heard around about the powerful, flexible, functionally complete PYTHON UNIVERSE”, encompassing an advanced Object-Oriented Language and a very wide family of packages, I decided to run an exercise about a problem I've been tackling since my youth (have a look at the Bibliography). I succeeded in completing it in a few days and I'm attaching my solution to the problem of finding the points in a sample that are "central" in a surrounding topological neighborhood. They are eligible as centroids for a Cluster Analysis after the aggregation of "too near points'. The solution is based on the search of potential wells in a suitable potential field, similar to the one all of us studied in high school. Therefore, too near points may be in the same potential well. No more words, have a look at the attachment. My coding is that of a beginner. I'm sure everybody would find more efficient coding. As a comment: I started studying Python around May 15th 2023. My best regards. Ulderico Santarelli. SAMPLE POINTS CENTRALITY INDEX.docx Description: MS-Word 2007 document ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn